Foundation Models for Generalizable Physiological Signal Representation in Wearable Health Applications
Keywords:
foundation models; wearable health; physiological signals; self-supervised learning; federated learning; fairness; deploymentAbstract
Wearable health systems generate continuous photoplethysmographic, electrocardiographic, accelerometric, and related physiological signals across highly diverse populations, device configurations, and environmental conditions. Conventional supervised models trained on isolated datasets often fail to generalize because physiological patterns vary with sensor placement, skin tone, motion, pathology, and recording hardware. Foundation models, which learn transferable representations from large-scale multimodal data, offer a promising system-level alternative. This paper examines the architectural, governance, deployment, robustness, fairness, and sustainability dimensions of foundation models for physiological signal representation. It discusses self-supervised pretraining, temporal and multimodal fusion, domain adaptation, and the structural trade-offs between centralized training and decentralized edge deployment. The analysis emphasizes that clinical utility depends not only on accuracy but also on data governance, regulatory alignment, infrastructure resilience, and equity. Wearable photoplethysmography and motion artifact mitigation serve as a representative challenge, while the discussion remains situated within broader wearable health infrastructure. The paper argues that sustainable foundation model ecosystems require careful coordination among data curation, federated learning, model auditing, and post-deployment monitoring. It concludes with future directions for cross-institutional collaboration, interoperable evaluation, and context-aware personalization.
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