SONIC: Synergizing VisiON Foundation Models for Stress RecogNItion from ECG signals
Abstract: Stress recognition through physiological signals such as Electrocardiogram (ECG) signals has garnered significant attention. Traditionally, research in this field predominantly focused on utilizing handcrafted features or raw signals as inputs for learning algorithms. However, there is now a burgeoning interest within the community in leveraging large-scale vision foundation models (VFMs) like ResNet50, VGG19, and others. These VFMs are increasingly preferred due to their ability to capture complex features, enhancing the accuracy and effectiveness of stress recognition systems. However, no particular focus has been given on combining these VFMs. The combination of VFMs offers promising benefits by harnessing their collective knowledge to extract richer representations for improved stress recognition. So, to mitigate this research gap, we focus on combining different VFMs for stress recognition from ECG and propose SONIC, a novel framework that combines VFMs through their logits and training a fully connected network on the combined logits. Through extensive experimentation, SONIC showed the top performance against individual VFMs performance on the WESAD benchmark. With SONIC, we report state-of-the-art (SOTA) performance in WESAD with 99.36% and 99.24% (stress vs non-stress) and 97.66% and 97.10% (amusement vs stress vs baseline) in accuracy and F1 respectively.
- “Psychological stress and disease,” Jama, vol. 298, no. 14, pp. 1685–1687, 2007.
- “Environmental noise-induced effects on stress hormones, oxidative stress, and vascular dysfunction: key factors in the relationship between cerebrocardiovascular and psychological disorders,” Oxidative medicine and cellular longevity, vol. 2019, 2019.
- “Stress and cancer. part i: Mechanisms mediating the effect of stressors on cancer,” Journal of neuroimmunology, vol. 346, pp. 577311, 2020.
- “Best practices for stress measurement: How to measure psychological stress in health research,” Health psychology open, vol. 7, no. 2, pp. 2055102920933072, 2020.
- “Speaker Embeddings as Individuality Proxy for Voice Stress Detection,” in Proc. INTERSPEECH 2023, 2023, pp. 1838–1842.
- “Stress recognition using facial landmarks and cnn (alexnet),” in Journal of Physics: Conference Series. IOP Publishing, 2021, vol. 2089, p. 012039.
- “Attention based hybrid deep learning model for wearable based stress recognition,” Engineering Applications of Artificial Intelligence, vol. 127, pp. 107391, 2024.
- “Introducing wesad, a multimodal dataset for wearable stress and affect detection,” in Proceedings of the 20th ACM international conference on multimodal interaction, 2018, pp. 400–408.
- “Stressid: a multimodal dataset for stress identification,” Advances in Neural Information Processing Systems, vol. 36, 2024.
- “Comparison of stress detection through ecg and ppg signals using a random forest-based algorithm,” in 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022, pp. 3150–3153.
- “A novel multi-kernel 1d convolutional neural network for stress recognition from ecg,” in 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). IEEE, 2019, pp. 1–4.
- “Real-time psychological stress detection according to ecg using deep learning,” Applied Sciences, vol. 11, no. 9, pp. 3838, 2021.
- “Psychological stress detection according to ecg using a deep learning model with attention mechanism,” Applied Sciences, vol. 11, no. 6, pp. 2848, 2021.
- “Deep ecg-respiration network (deeper net) for recognizing mental stress,” Sensors, vol. 19, no. 13, pp. 3021, 2019.
- “A transformer architecture for stress detection from ecg,” in Proceedings of the 2021 ACM International Symposium on Wearable Computers, 2021, pp. 132–134.
- “Detecting stress through 2d ecg images using pretrained models, transfer learning and model compression techniques,” Machine Learning with Applications, vol. 10, pp. 100395, 2022.
- “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
- “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
- “Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting,” Frontiers in Artificial Intelligence, vol. 6, 2023.
- “Vit-deit: An ensemble model for breast cancer histopathological images classification,” in 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC). IEEE, 2023, pp. 1–6.
- “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
- “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning. PMLR, 2019, pp. 6105–6114.
- “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
- “Do better imagenet models transfer better?,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 2661–2671.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.