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Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives
Published 5 Mar 2024 in cs.LG, cs.AI, cs.CV, and cs.CY | (2403.02772v2)
Abstract: Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. AI-driven virtual rehabilitation, which allows patients to independently complete exercises at home, utilizes AI algorithms to analyze exercise data, providing feedback to patients and updating clinicians on their progress. These programs commonly prescribe a variety of exercise types, leading to a distinct challenge in rehabilitation exercise assessment datasets: while abundant in overall training samples, these datasets often have a limited number of samples for each individual exercise type. This disparity hampers the ability of existing approaches to train generalizable models with such a small sample size per exercise type. Addressing this issue, this paper introduces a novel supervised contrastive learning framework with hard and soft negative samples that effectively utilizes the entire dataset to train a single model applicable to all exercise types. This model, with a Spatial-Temporal Graph Convolutional Network (ST-GCN) architecture, demonstrated enhanced generalizability across exercises and a decrease in overall complexity. Through extensive experiments on three publicly available rehabilitation exercise assessment datasets, UI-PRMD, IRDS, and KIMORE, our method has proven to surpass existing methods, setting a new benchmark in rehabilitation exercise quality assessment.
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Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Frazzitta, G., Balbi, P., Maestri, R., Bertotti, G., Boveri, N., Pezzoli, G.: The beneficial role of intensive exercise on parkinson disease progression. American Journal of Physical Medicine and Rehabilitation 92(6), 523–532 (2013) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M., Paul, D., Baker, R.: A review of computational approaches for evaluation of rehabilitation exercises. Computers in biology and medicine 119, 103687 (2020) Shanmugasegaram et al. [2012] Shanmugasegaram, S., Gagliese, L., Oh, P., Stewart, D.E., Brister, S.J., Chan, V., Grace, S.L.: Psychometric validation of the cardiac rehabilitation barriers scale. Clinical rehabilitation 26(2), 152–164 (2012) Shirozhan et al. [2022] Shirozhan, S., Arsalani, N., Maddah, S.S.B., Mohammadi-Shahboulaghi, F.: Barriers and facilitators of rehabilitation nursing care for patients with disability in the rehabilitation hospital: A qualitative study. Frontiers in Public Health 10 (2022) Combes et al. [2018] Combes, J.-B., Elliott, R.F., Skåtun, D.: Hospital staff shortage: the role of the competitiveness of pay of different groups of nursing staff on staff shortage. Applied Economics 50(60), 6547–6552 (2018) Ferreira et al. [2023] Ferreira, R., Santos, R., Sousa, A.: Usage of auxiliary systems and artificial intelligence in home-based rehabilitation: A review. Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare, 163–196 (2023) Krasovsky et al. [2020] Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M., Paul, D., Baker, R.: A review of computational approaches for evaluation of rehabilitation exercises. Computers in biology and medicine 119, 103687 (2020) Shanmugasegaram et al. [2012] Shanmugasegaram, S., Gagliese, L., Oh, P., Stewart, D.E., Brister, S.J., Chan, V., Grace, S.L.: Psychometric validation of the cardiac rehabilitation barriers scale. Clinical rehabilitation 26(2), 152–164 (2012) Shirozhan et al. [2022] Shirozhan, S., Arsalani, N., Maddah, S.S.B., Mohammadi-Shahboulaghi, F.: Barriers and facilitators of rehabilitation nursing care for patients with disability in the rehabilitation hospital: A qualitative study. Frontiers in Public Health 10 (2022) Combes et al. [2018] Combes, J.-B., Elliott, R.F., Skåtun, D.: Hospital staff shortage: the role of the competitiveness of pay of different groups of nursing staff on staff shortage. 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[2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shanmugasegaram, S., Gagliese, L., Oh, P., Stewart, D.E., Brister, S.J., Chan, V., Grace, S.L.: Psychometric validation of the cardiac rehabilitation barriers scale. Clinical rehabilitation 26(2), 152–164 (2012) Shirozhan et al. [2022] Shirozhan, S., Arsalani, N., Maddah, S.S.B., Mohammadi-Shahboulaghi, F.: Barriers and facilitators of rehabilitation nursing care for patients with disability in the rehabilitation hospital: A qualitative study. Frontiers in Public Health 10 (2022) Combes et al. [2018] Combes, J.-B., Elliott, R.F., Skåtun, D.: Hospital staff shortage: the role of the competitiveness of pay of different groups of nursing staff on staff shortage. Applied Economics 50(60), 6547–6552 (2018) Ferreira et al. [2023] Ferreira, R., Santos, R., Sousa, A.: Usage of auxiliary systems and artificial intelligence in home-based rehabilitation: A review. Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare, 163–196 (2023) Krasovsky et al. [2020] Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shirozhan, S., Arsalani, N., Maddah, S.S.B., Mohammadi-Shahboulaghi, F.: Barriers and facilitators of rehabilitation nursing care for patients with disability in the rehabilitation hospital: A qualitative study. Frontiers in Public Health 10 (2022) Combes et al. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. 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IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. 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[2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. 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In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. 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[2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. 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[2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. 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[2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M., Paul, D., Baker, R.: A review of computational approaches for evaluation of rehabilitation exercises. Computers in biology and medicine 119, 103687 (2020) Shanmugasegaram et al. [2012] Shanmugasegaram, S., Gagliese, L., Oh, P., Stewart, D.E., Brister, S.J., Chan, V., Grace, S.L.: Psychometric validation of the cardiac rehabilitation barriers scale. 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[2020] Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shanmugasegaram, S., Gagliese, L., Oh, P., Stewart, D.E., Brister, S.J., Chan, V., Grace, S.L.: Psychometric validation of the cardiac rehabilitation barriers scale. Clinical rehabilitation 26(2), 152–164 (2012) Shirozhan et al. [2022] Shirozhan, S., Arsalani, N., Maddah, S.S.B., Mohammadi-Shahboulaghi, F.: Barriers and facilitators of rehabilitation nursing care for patients with disability in the rehabilitation hospital: A qualitative study. Frontiers in Public Health 10 (2022) Combes et al. [2018] Combes, J.-B., Elliott, R.F., Skåtun, D.: Hospital staff shortage: the role of the competitiveness of pay of different groups of nursing staff on staff shortage. Applied Economics 50(60), 6547–6552 (2018) Ferreira et al. [2023] Ferreira, R., Santos, R., Sousa, A.: Usage of auxiliary systems and artificial intelligence in home-based rehabilitation: A review. Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare, 163–196 (2023) Krasovsky et al. [2020] Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Combes, J.-B., Elliott, R.F., Skåtun, D.: Hospital staff shortage: the role of the competitiveness of pay of different groups of nursing staff on staff shortage. Applied Economics 50(60), 6547–6552 (2018) Ferreira et al. [2023] Ferreira, R., Santos, R., Sousa, A.: Usage of auxiliary systems and artificial intelligence in home-based rehabilitation: A review. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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[2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. 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[2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. 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[2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Combes, J.-B., Elliott, R.F., Skåtun, D.: Hospital staff shortage: the role of the competitiveness of pay of different groups of nursing staff on staff shortage. Applied Economics 50(60), 6547–6552 (2018) Ferreira et al. [2023] Ferreira, R., Santos, R., Sousa, A.: Usage of auxiliary systems and artificial intelligence in home-based rehabilitation: A review. 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[2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. 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[2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. 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[2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. 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[2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. 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Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shirozhan, S., Arsalani, N., Maddah, S.S.B., Mohammadi-Shahboulaghi, F.: Barriers and facilitators of rehabilitation nursing care for patients with disability in the rehabilitation hospital: A qualitative study. 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[2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Combes, J.-B., Elliott, R.F., Skåtun, D.: Hospital staff shortage: the role of the competitiveness of pay of different groups of nursing staff on staff shortage. Applied Economics 50(60), 6547–6552 (2018) Ferreira et al. [2023] Ferreira, R., Santos, R., Sousa, A.: Usage of auxiliary systems and artificial intelligence in home-based rehabilitation: A review. Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare, 163–196 (2023) Krasovsky et al. [2020] Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. 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IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. 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IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. 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[2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. 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[2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. 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Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Krasovsky, T., Lubetzky, A.V., Archambault, P.S., Wright, W.G.: Will virtual rehabilitation replace clinicians: a contemporary debate about technological versus human obsolescence. Journal of NeuroEngineering and Rehabilitation 17(1), 1–8 (2020) Seron et al. [2021] Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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[2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. 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[2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. 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[2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Seron, P., Oliveros, M.-J., Gutierrez-Arias, R., Fuentes-Aspe, R., Torres-Castro, R.C., Merino-Osorio, C., Nahuelhual, P., Inostroza, J., Jalil, Y., Solano, R., et al.: Effectiveness of telerehabilitation in physical therapy: a rapid overview. Physical therapy 101(6), 053 (2021) Boukhennoufa et al. [2022] Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. [2024] Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., McDonald-Maier, K.D.: Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 71, 103197 (2022) Abedi et al. 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[2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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[2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. 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[2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Colella, T.J., Pakosh, M., Khan, S.S.: Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digital Medicine 7(1), 25 (2024) Sangani et al. [2020] Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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[2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. 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[2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. 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[2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sangani, S., Patterson, K.K., Fung, J., Lamontagne, A., et al.: Real-time avatar-based feedback to enhance the symmetry of spatiotemporal parameters after stroke: Instantaneous effects of different avatar views. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(4), 878–887 (2020) Sardari et al. [2023] Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835 (2023) Fernandez-Cervantes et al. [2018] Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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[2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. 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Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V., Duncan, M.J.: Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. 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[2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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[2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. 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[2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. 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[2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Fernandez-Cervantes, V., Neubauer, N., Hunter, B., Stroulia, E., Liu, L.: Virtualgym: A kinect-based system for seniors exercising at home. Entertainment Computing 27, 60–72 (2018) Abedi et al. [2023a] Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Bisht, P., Chatterjee, R., Agrawal, R., Sharma, V., Jayagopi, D., Khan, S.S.: Rehabilitation exercise repetition segmentation and counting using skeletal body joints. In: 2023 20th Conference on Robots and Vision (CRV), pp. 288–295. IEEE Computer Society, Los Alamitos, CA, USA (2023). https://doi.org/10.1109/CRV60082.2023.00044 . https://doi.ieeecomputersociety.org/10.1109/CRV60082.2023.00044 Abedi et al. [2023b] Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Abedi, A., Malmirian, M., Khan, S.S.: Cross-modal video to body-joints augmentation for rehabilitation exercise quality assessment. arXiv preprint arXiv:2306.09546 (2023) Capecci et al. [2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. 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[2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. 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In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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[2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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[2019] Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Monteriu, A., Romeo, L., Verdini, F.: The kimore dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(7), 1436–1448 (2019) https://doi.org/10.1109/TNSRE.2019.2923060 . Epub 2019 Jun 14 Pavllo et al. [2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). 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[2019] Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. 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[2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3d human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7753–7762 (2019) Lugaresi et al. [2019] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., et al.: Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019) Yan et al. [2018] Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. 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In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Yao et al. [2023] Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Yao, L., Lei, Q., Zhang, H., Du, J., Gao, S.: A contrastive learning network for performance metric and assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Zheng et al. [2023] Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). 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[2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Zheng, K., Wu, J., Zhang, J., Guo, C.: A skeleton-based rehabilitation exercise assessment system with rotation invariance. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Deb et al. [2022] Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. 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Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Deb, S., Islam, M.F., Rahman, S., Rahman, S.: Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 30, 410–419 (2022) Vakanski et al. [2018] Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. 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In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Vakanski, A., Jun, H.-p., Paul, D., Baker, R.: A data set of human body movements for physical rehabilitation exercises. Data 3(1) (2018) https://doi.org/10.3390/data3010002 Miron et al. [2021] Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Miron, A., Sadawi, N., Ismail, W., Hussain, H., Grosan, C.: Intellirehabds (irds)—a dataset of physical rehabilitation movements. Data 6(5) (2021) https://doi.org/10.3390/data6050046 Khanghah et al. [2023] Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. 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[2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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[2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. 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In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Khanghah, A.B., Fernie, G., Fekr, A.R.: A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms. Machine Learning with Applications 14, 100499 (2023) Khosla et al. [2021] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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[2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised Contrastive Learning (2021) Robinson et al. [2021] Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Robinson, J., Chuang, C.-Y., Sra, S., Jegelka, S.: Contrastive Learning with Hard Negative Samples (2021) Liao et al. [2020] Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. 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CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(2), 468–477 (2020) Bashir et al. [2005] Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. 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In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Bashir, F., Qu, W., Khokhar, A., Schonfeld, D.: Hmm-based motion recognition system using segmented pca. In: IEEE International Conference on Image Processing 2005, vol. 3, p. 1288 (2005). IEEE Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Lin, L., Zhang, J., Liu, J.: Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2363–2372 (2023) Guo and Khan [2021] Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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[2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. [2023] Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Guo, Q., Khan, S.S.: Exercise-specific feature extraction approach for assessing physical rehabilitation. In: 4th IJCAI Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living. IJCAI (2021) Karagoz et al. 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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. 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[2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Karagoz, B., Ashraf, A., Khan, S.: Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises datasets. preprint (2023) https://doi.org/10.13140/RG.2.2.15642.21447 Zha et al. [2024] Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. 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In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
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IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. 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[2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zha, K., Cao, P., Son, J., Yang, Y., Katabi, D.: Rank-n-contrast: Learning continuous representations for regression. Advances in Neural Information Processing Systems 36 (2024) Selvaraju et al. [2017] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017) Réby et al. [2023] Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. 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IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. [2021] Khan, S.S., Shen, Z., Sun, H., Patel, A., Abedi, A.: Modified supervised contrastive learning for detecting anomalous driving behaviours. CoRR abs/2109.04021 (2021) 2109.04021 Kopuklu et al. [2021] Kopuklu, O., Zheng, J., Xu, H., Rigoll, G.: Driver anomaly detection: A dataset and contrastive learning approach. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 91–100 (2021) Guo et al. [2021] Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition (2021) Rao et al. [2021] Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (2021) Kingma and Ba [2017] Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Réby, K., Dulau, I., Dubrasquet, G., Aimar, M.B.: Graph transformer for physical rehabilitation evaluation. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2023). IEEE Mourchid and Slama [2023a] Mourchid, Y., Slama, R.: Mr-stgn: Multi-residual spatio temporal graph network using attention fusion for patient action assessment. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6 (2023). IEEE Mourchid and Slama [2023b] Mourchid, Y., Slama, R.: D-stgcnt: A dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Computers in Biology and Medicine 165, 107420 (2023) Li et al. [2023] Li, C., Ling, X., Xia, S.: A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises. In: International Conference on Artificial Neural Networks, pp. 229–240 (2023). Springer Shi et al. [2020] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Transactions on Image Processing 29, 9532–9545 (2020) Lin et al. [2023] Lin, L., Zhang, J., Liu, J.: Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition (2023) Capecci et al. [2018] Capecci, M., Ceravolo, M.G., Ferracuti, F., Grugnetti, M., Iarlori, S., Longhi, S., Romeo, L., Verdini, F.: An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept. Journal of biomechanics 69, 70–80 (2018) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Khan et al. 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- Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E.Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) 1912.01703 Zhang et al. [2019] Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (2019) Tasnim et al. [2020] Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Tasnim, N., Islam, M.M., Baek, J.-H.: Deep learning-based action recognition using 3d skeleton joints information. Inventions 5(3) (2020) https://doi.org/10.3390/inventions5030049 van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(86), 2579–2605 (2008) Das and Ortega [2022] Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Das, P., Ortega, A.: Gradient-weighted class activation mapping for spatio temporal graph convolutional network. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4043–4047 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746621 Zhao et al. [2022] Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022) Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
- Zhao, Z., Kiciroglu, S., Vinzant, H., Cheng, Y., Katircioglu, I., Salzmann, M., Fua, P.: 3d pose based feedback for physical exercises. In: Proceedings of the Asian Conference on Computer Vision, pp. 1316–1332 (2022)
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