Foundation Model-Based Physiological Signal Representation for Generalizable Wearable Health Applications
Keywords:
foundation models; physiological signal representation; wearable health; self-supervised learning; federated learning; fairness; sustainabilityAbstract
Wearable health devices generate continuous, multimodal physiological signals that are noisy, context-dependent, and highly heterogeneous across individuals and sensing hardware. Foundation models offer a promising route toward generalizable physiological signal representation by learning transferable features from large-scale self-supervised pretraining. However, translating this promise into real-world wearable health applications requires careful attention to architecture, data governance, deployment infrastructure, robustness, fairness, sustainability, and regulatory oversight. This paper presents a system-level analysis of foundation model-based physiological signal representation. It examines the structural trade-offs between centralized and federated learning, the balance between representation capacity and on-device efficiency, and the challenges of distribution shift induced by motion, sensor placement, skin tone, and disease prevalence. The discussion integrates insights from language and vision foundation models, medical artificial intelligence, and wearable sensing. It emphasizes that physiological foundation models are not merely computational artifacts but socio-technical infrastructures that require lifecycle governance. The paper argues for a coordinated design perspective that couples representation learning with transparency mechanisms, energy-aware model updates, equity auditing, and clinical validation. Such a perspective is necessary to ensure that wearable health foundation models achieve generalizability without exacerbating existing disparities or creating unmanageable operational burdens.
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