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Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera (2401.10037v1)
Published 18 Jan 2024 in cs.CV
Abstract: Purpose: In this paper, we present a novel approach to the automatic evaluation of open surgery skills using depth cameras. This work is intended to show that depth cameras achieve similar results to RGB cameras, which is the common method in the automatic evaluation of open surgery skills. Moreover, depth cameras offer advantages such as robustness to lighting variations, camera positioning, simplified data compression, and enhanced privacy, making them a promising alternative to RGB cameras. Methods: Experts and novice surgeons completed two simulators of open suturing. We focused on hand and tool detection, and action segmentation in suturing procedures. YOLOv8 was used for tool detection in RGB and depth videos. Furthermore, UVAST and MSTCN++ were used for action segmentation. Our study includes the collection and annotation of a dataset recorded with Azure Kinect. Results: We demonstrated that using depth cameras in object detection and action segmentation achieves comparable results to RGB cameras. Furthermore, we analyzed 3D hand path length, revealing significant differences between experts and novice surgeons, emphasizing the potential of depth cameras in capturing surgical skills. We also investigated the influence of camera angles on measurement accuracy, highlighting the advantages of 3D cameras in providing a more accurate representation of hand movements. Conclusion: Our research contributes to advancing the field of surgical skill assessment by leveraging depth cameras for more reliable and privacy evaluations. The findings suggest that depth cameras can be valuable in assessing surgical skills and provide a foundation for future research in this area.
- Dosis, A., Aggarwal, R., Bello, F., Moorthy, K., Munz, Y., Gillies, D., Darzi, A.: Synchronized video and motion analysis for the assessment of procedures in the operating theater. Archives of Surgery 140(3), 293–299 (2005) Smith et al. [2002] Smith, S., Torkington, J., Brown, T., Taffinder, N., Darzi, A.: Motion analysis: a tool for assessing laparoscopic dexterity in the performance of a laboratory-based laparoscopic cholecystectomy. Surgical endoscopy 16, 640–645 (2002) D’Angelo et al. [2016] D’Angelo, A.-L.D., Rutherford, D.N., Ray, R.D., Laufer, S., Mason, A., Pugh, C.M.: Working volume: validity evidence for a motion-based metric of surgical efficiency. The American Journal of Surgery 211(2), 445–450 (2016) Goldbraikh et al. [2022] Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Smith, S., Torkington, J., Brown, T., Taffinder, N., Darzi, A.: Motion analysis: a tool for assessing laparoscopic dexterity in the performance of a laboratory-based laparoscopic cholecystectomy. Surgical endoscopy 16, 640–645 (2002) D’Angelo et al. [2016] D’Angelo, A.-L.D., Rutherford, D.N., Ray, R.D., Laufer, S., Mason, A., Pugh, C.M.: Working volume: validity evidence for a motion-based metric of surgical efficiency. The American Journal of Surgery 211(2), 445–450 (2016) Goldbraikh et al. [2022] Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) D’Angelo, A.-L.D., Rutherford, D.N., Ray, R.D., Laufer, S., Mason, A., Pugh, C.M.: Working volume: validity evidence for a motion-based metric of surgical efficiency. The American Journal of Surgery 211(2), 445–450 (2016) Goldbraikh et al. [2022] Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
- Smith, S., Torkington, J., Brown, T., Taffinder, N., Darzi, A.: Motion analysis: a tool for assessing laparoscopic dexterity in the performance of a laboratory-based laparoscopic cholecystectomy. Surgical endoscopy 16, 640–645 (2002) D’Angelo et al. [2016] D’Angelo, A.-L.D., Rutherford, D.N., Ray, R.D., Laufer, S., Mason, A., Pugh, C.M.: Working volume: validity evidence for a motion-based metric of surgical efficiency. The American Journal of Surgery 211(2), 445–450 (2016) Goldbraikh et al. [2022] Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) D’Angelo, A.-L.D., Rutherford, D.N., Ray, R.D., Laufer, S., Mason, A., Pugh, C.M.: Working volume: validity evidence for a motion-based metric of surgical efficiency. The American Journal of Surgery 211(2), 445–450 (2016) Goldbraikh et al. [2022] Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. 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[2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. 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[2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
- D’Angelo, A.-L.D., Rutherford, D.N., Ray, R.D., Laufer, S., Mason, A., Pugh, C.M.: Working volume: validity evidence for a motion-based metric of surgical efficiency. The American Journal of Surgery 211(2), 445–450 (2016) Goldbraikh et al. [2022] Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). 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The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
- Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. International Journal of Computer Assisted Radiology and Surgery 17(3), 437–448 (2022) Fathabadi et al. [2021] Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
- Fathabadi, F.R., Grantner, J.L., Shebrain, S.A., Abdel-Qader, I.: Multi-class detection of laparoscopic instruments for the intelligent box-trainer system using faster r-cnn architecture. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000149–000154 (2021). IEEE Goldbraikh et al. [2022] Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. 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[2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. 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[2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
- Goldbraikh, A., Avisdris, N., Pugh, C.M., Laufer, S.: Bounded future ms-tcn++ for surgical gesture recognition. In: European Conference on Computer Vision, pp. 406–421 (2022). Springer Halperin et al. [2023] Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. 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[2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Halperin, L., Sroka, G., Zuckerman, I., Laufer, S.: Automatic performance evaluation of the intracorporeal suture exercise. International Journal of Computer Assisted Radiology and Surgery, 1–4 (2023) Bkheet et al. [2023] Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. 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[2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. 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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
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[2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Bkheet, E., D’Angelo, A.-L., Goldbraikh, A., Laufer, S.: Using hand pose estimation to automate open surgery training feedback. International Journal of Computer Assisted Radiology and Surgery, 1–7 (2023) Dascalaki et al. [2009] Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Dascalaki, E.G., Gaglia, A.G., Balaras, C.A., Lagoudi, A.: Indoor environmental quality in hellenic hospital operating rooms. Energy and Buildings 41(5), 551–560 (2009) https://doi.org/10.1016/j.enbuild.2008.11.023 Likitlersuang et al. [2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. 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[2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. 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[2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
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[2017] Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
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Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Likitlersuang, J., Sumitro, E.R., Theventhiran, P., Kalsi-Ryan, S., Zariffa, J.: Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community. The journal of spinal cord medicine 40(6), 706–714 (2017) Haque et al. [2020] Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. 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IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Haque, A., Milstein, A., Fei-Fei, L.: Illuminating the dark spaces of healthcare with ambient intelligence. Nature 585(7824), 193–202 (2020) Sun et al. [2023] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3200–3225 (2023) https://doi.org/10.1109/TPAMI.2022.3183112 Yeung et al. [2019] Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. 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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? 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[2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. 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Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. 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[2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. 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Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. 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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. 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Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. 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Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Bianconi, G.M., Alahi, A., Lee, J., et al.: A computer vision system for deep learning-based detection of patient mobilization activities in the icu. NPJ digital medicine 2(1), 11 (2019) Martinez-Martin et al. [2021] Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
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[2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
- Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S.S., Wieten, S., Cho, M.K., Magnus, D., Fei-Fei, L., et al.: Ethical issues in using ambient intelligence in health-care settings. The lancet digital health 3(2), 115–123 (2021) Siddiqi et al. [2021] Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
- Siddiqi, M.H., Almashfi, N., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., Kamruzzaman, M.: A unified approach for patient activity recognition in healthcare using depth camera. IEEE Access 9, 92300–92317 (2021) Williams et al. [2020] Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Williams, T.P., Snyder, C.L., Hancock, K.J., Iglesias, N.J., Sommerhalder, C., DeLao, S.C., Chacin, A.C., Perez, A.: Development of a low-cost, high-fidelity skin model for suturing. Journal of Surgical Research 256, 618–622 (2020) Buckarma et al. [2016] Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. 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[2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Buckarma, E., et al.: The How To Book of Low Cost Surgical Simulation, (2016). https://surgicaleducation.mayo.edu/how-to-book/ Biewald [2020] Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/ Jocher et al. [2023] Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics. https://github.com/ultralytics/ultralytics Behrmann et al. [2022] Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. 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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. 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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. 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Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Behrmann, N., Golestaneh, S.A., Kolter, Z., Gall, J., Noroozi, M.: Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In: European Conference on Computer Vision, pp. 52–68 (2022). Springer Li et al. [2020] Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Li, S.-J., AbuFarha, Y., Liu, Y., Cheng, M.-M., Gall, J.: Ms-tcn++: Multi-stage temporal convolutional network for action segmentation. IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. 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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. 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Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
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IEEE transactions on pattern analysis and machine intelligence (2020) Carreira and Zisserman [2017] Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. [2008] Chmarra, M.K., Jansen, F.W., Grimbergen, C.A., Dankelman, J.: Retracting and seeking movements during laparoscopic goal-oriented movements. is the shortest path length optimal? Surgical endoscopy 22, 943–949 (2008) Zhou et al. 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International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020)
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Surgical endoscopy 22, 943–949 (2008) Zhou et al. [2018] Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018) Lefor et al. [2020] Lefor, A.K., Harada, K., Dosis, A., Mitsuishi, M.: Motion analysis of the jhu-isi gesture and skill assessment working set using robotics video and motion assessment software. International Journal of Computer Assisted Radiology and Surgery 15, 2017–2025 (2020) Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017) Kay et al. [2017] Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017) Forney [1973] Forney, G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973) Chmarra et al. 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