Contrastive Learning for Motion-Robust Representation of Wearable Photoplethysmographic Signals
Keywords:
contrastive learning; photoplethysmography; wearable health monitoring; motion artifact; fairness; governance; infrastructureAbstract
Wearable photoplethysmographic sensors have become central to continuous health monitoring, but their real-world reliability is undermined by motion artifacts, sensor displacement, skin variability, and environmental interference. Conventional signal processing pipelines rely on heuristic filtering and accelerometer-assisted noise rejection that often fail under nonstationary daily activity. This paper examines contrastive learning as a systems-oriented strategy for learning motion-robust representations from wearable photoplethysmographic signals. By constructing latent spaces in which augmented views of the same physiological context remain close while dissimilar contexts are separated, contrastive models can suppress nuisance distortions while preserving diagnostically relevant signal structure. The analysis extends beyond algorithmic accuracy to address architectural trade-offs, edge-cloud partitioning, training infrastructure, governance, fairness, sustainability, and regulatory implications. It argues that motion-robust representation is not merely a model-level property but a structural outcome of careful augmentation design, encoder selection, deployment architecture, and continuous monitoring. The paper further considers how federated learning and on-device inference can support privacy-preserving representation learning, while stratified evaluation and governance mechanisms are needed to avoid widening health disparities. By integrating technical and policy perspectives, the discussion provides a research agenda for building robust, equitable, and maintainable wearable sensing infrastructures.
References
1. Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement, 28(3), R1–R39. https://doi.org/10.1088/0967-3334/28/3/R01
2. Zhang, Z., Pi, Z., & Liu, B. (2015). TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 62(2), 522–531. https://doi.org/10.1109/TBME.2014.2359372
3. Biswas, D., Simões-Capela, N., Van Hoof, C., & Van Helleputte, N. (2019). Heart rate estimation from wrist-worn photoplethysmography: A review. IEEE Sensors Journal, 19(16), 6560–6570. https://doi.org/10.1109/JSEN.2019.2914166
4. Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020). Investigating sources of inaccuracy in wearable optical heart rate sensors. npj Digital Medicine, 3, 18. https://doi.org/10.1038/s41746-020-0226-6
5. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning, 1597–1607.
6. Oord, A. v. d., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.
7. Kiyasseh, D., Zhu, T., & Clifton, D. A. (2021). CLOCS: Contrastive learning of cardiac signals across space, time, and patients. Proceedings of the International Conference on Machine Learning, 5606–5615.
8. Sarkar, P., & Etemad, A. (2020). Self-supervised ECG representation learning for emotion recognition. IEEE Transactions on Affective Computing, 13(3), 1541–1554.
9. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3, 119. https://doi.org/10.1038/s41746-020-00323-1
10. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
11. Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. International Conference on Learning Representations.
12. Zheng, X., Hu, S., Dwyer, V., Barrett, L., & Derakhshani, M. (2026). Joint attention mechanism learning to facilitate opto-physiological monitoring during physical activity. Biomedical Signal Processing and Control, 113, 108949.
13. Banbury, C., Reddi, V. J., Lam, M., Fu, W., Fazel, A., Holleman, J., ... & Whatmough, P. (2020). Benchmarking TinyML systems: Challenges and direction. arXiv preprint arXiv:2003.04821.
14. Wu, M., & Luo, J. (2019). Wearable technology applications in healthcare: A literature review. Online Journal of Nursing Informatics, 23(3).
15. Yadav, P., Steinbach, M., Kumar, V., & Simon, G. (2018). Mining electronic health records (EHRs): A survey. ACM Computing Surveys, 50(6), 1–40.
16. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
17. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.
18. Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689.
19. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
20. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273–1282.
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