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Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones (2404.05003v1)

Published 7 Apr 2024 in cs.CV and cs.AI

Abstract: Remote photoplethysmography (rPPG) emerges as a promising method for non-invasive, convenient measurement of vital signs, utilizing the widespread presence of cameras. Despite advancements, existing datasets fall short in terms of size and diversity, limiting comprehensive evaluation under diverse conditions. This paper presents an in-depth analysis of the VitalVideo dataset, the largest real-world rPPG dataset to date, encompassing 893 subjects and 6 Fitzpatrick skin tones. Our experimentation with six unsupervised methods and three supervised models demonstrates that datasets comprising a few hundred subjects(i.e., 300 for UBFC-rPPG, 500 for PURE, and 700 for MMPD-Simple) are sufficient for effective rPPG model training. Our findings highlight the importance of diversity and consistency in skin tones for precise performance evaluation across different datasets.

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References (29)
  1. High-throughput, contact-free detection of atrial fibrillation from video with deep learning, JAMA cardiology 5 (2020) 105–107.
  2. Non-contact physiological monitoring of preterm infants in the neonatal intensive care unit, npj Digital Medicine 2 (2019) 1–18.
  3. rppg-toolbox: Deep remote ppg toolbox, arXiv preprint arXiv:2210.00716 (2022).
  4. Algorithmic principles of remote ppg, IEEE Transactions on Biomedical Engineering 64 (2016) 1479–1491.
  5. Robust efficient estimation of heart rate pulse from video, Biomedical optics express 5 (2014) 1124–1135.
  6. C. Takano, Y. Ohta, Heart rate measurement based on a time-lapse image, Medical engineering & physics 29 (2007) 853–857.
  7. A meta-analysis of the impact of skin tone and gender on non-contact photoplethysmography measurements, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 284–285.
  8. Non-contact video-based pulse rate measurement on a mobile service robot, in: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE, 2014, pp. 1056–1062.
  9. Unsupervised skin tissue segmentation for remote photoplethysmography, Pattern Recognition Letters 124 (2019) 82–90.
  10. D. McDuff, Camera measurement of physiological vital signs, ACM Computing Surveys (CSUR) (2021).
  11. Remote plethysmographic imaging using ambient light., Optics express 16 (2008) 21434–21445.
  12. Advancements in noncontact, multiparameter physiological measurements using a webcam, IEEE transactions on biomedical engineering 58 (2010) 7–11.
  13. G. De Haan, V. Jeanne, Robust pulse rate from chrominance-based rppg, IEEE Transactions on Biomedical Engineering 60 (2013) 2878–2886.
  14. G. De Haan, A. Van Leest, Improved motion robustness of remote-ppg by using the blood volume pulse signature, Physiological measurement 35 (2014) 1913.
  15. Local group invariance for heart rate estimation from face videos in the wild, in: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018, pp. 1254–1262.
  16. W. Chen, D. McDuff, DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks, arXiv:1805.07888 [cs] (2018). URL: http://arxiv.org/abs/1805.07888, arXiv: 1805.07888.
  17. Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 151–160.
  18. Multi-task temporal shift attention networks for on-device contactless vitals measurement, Advances in Neural Information Processing Systems 33 (2020) 19400–19411.
  19. Physformer: Facial video-based physiological measurement with temporal difference transformer, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 4186–4196.
  20. Advancing non-contact vital sign measurement using synthetic avatars, arXiv preprint arXiv:2010.12949 (2020).
  21. Synthetic generation of face videos with plethysmograph physiology, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20587–20596.
  22. Mobilephys: Personalized mobile camera-based contactless physiological sensing, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6 (2022). URL: https://doi.org/10.1145/3517225. doi:10.1145/3517225.
  23. Metaphys: few-shot adaptation for non-contact physiological measurement, in: Proceedings of the Conference on Health, Inference, and Learning, 2021, pp. 154–163.
  24. Federated remote physiological measurement with imperfect data, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 2155–2164.
  25. Z. Sun, X. Li, Contrast-phys: Unsupervised video-based remote physiological measurement via spatiotemporal contrast, in: European Conference on Computer Vision, Springer, 2022, pp. 492–510.
  26. J. Gideon, S. Stent, The way to my heart is through contrastive learning: Remote photoplethysmography from unlabelled video, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 3995–4004.
  27. Non-contrastive unsupervised learning of physiological signals from video, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14464–14474.
  28. Simper: Simple self-supervised learning of periodic targets, arXiv preprint arXiv:2210.03115 (2022).
  29. G. de Haan, A. van Leest, Improved motion robustness of remote-PPG by using the blood volume pulse signature, Physiological Measurement 35 (2014) 1913–1926. URL: https://doi.org/10.1088/0967-3334/35/9/1913. doi:10.1088/0967-3334/35/9/1913.
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