Self-Supervised Representation Learning for Motion-Resilient Wearable Biosignal Analysis
Keywords:
wearable biosignals; self-supervised learning; motion artifacts; representation learning; federated governance; fairnessAbstract
Wearable biosignal analysis has become a critical infrastructure for continuous health monitoring, yet motion artifacts, heterogeneous sensor configurations, and scarce clinical labels continue to limit the generalization of supervised deep models. This paper examines self-supervised representation learning as a system-level strategy for building motion-resilient biosignal analysis pipelines. Rather than focusing narrowly on model architectures, the discussion addresses the structural trade-offs among on-body sensing, edge computing, cloud aggregation, representation pretraining, downstream adaptation, and governance mechanisms. The central argument is that motion resilience is not solely a signal processing problem but an emergent property of a learning system that combines contrastive pretraining, temporal augmentation, multimodal synchronization, and context-aware deployment. The paper analyzes how representations learned from unlabeled ambulatory data can reduce dependence on curated datasets, support transfer across sensor placements and activity contexts, and improve robustness under distributional shift. It further considers infrastructure requirements, computational sustainability, regulatory constraints, fairness risks, and privacy-preserving federated learning. Drawing on recent advances in contrastive learning, physiological signal modeling, and decentralized systems, the paper provides a forward-looking perspective on the design of scalable, equitable, and governable biosignal learning infrastructures. The discussion emphasizes that technical performance must be evaluated together with deployment feasibility, energy efficiency, data governance, and societal implications to ensure that self-supervised biosignal representations are safe and effective outside controlled laboratory settings.
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