Self-Supervised Learning for Robust Physiological Signal Representation in Wearable Devices
Keywords:
self-supervised learning; wearable devices; physiological signal processing; representation learning; robustness; health data governanceAbstract
Wearable physiological monitoring has become a central element of continuous health assessment, yet the translation of raw biosignals into clinically meaningful representations remains constrained by noise, inter-subject variability, motion artifacts, and the high cost of labeled data. Self-supervised learning offers a structurally significant alternative by exploiting large volumes of unlabeled physiological recordings to learn representations that can be transferred across downstream tasks. This paper presents a system-level examination of self-supervised representation learning for wearable physiological signals, with emphasis on architectural design, deployment constraints, robustness, data governance, fairness, and long-term sustainability. The study connects contrastive, predictive, and generative self-supervised paradigms to the specific characteristics of electrocardiography, photoplethysmography, and related modalities. It further analyzes how these representations can be embedded within resource-constrained wearable ecosystems while preserving utility across heterogeneous populations and real-world operating conditions. Drawing on cross-domain evidence from computer vision, natural language processing, speech, and clinical time series analysis, the paper identifies structural trade-offs between representation quality, computational cost, adaptability, and regulatory accountability. The discussion includes model governance considerations, bias amplification risks, data provenance, and the environmental implications of large-scale pretraining. Rather than proposing a single algorithmic solution, the paper develops an interdisciplinary perspective on how self-supervised representation learning can support robust, equitable, and operationally viable physiological monitoring infrastructures. The conclusion outlines forward-looking research directions that integrate technical advancement with institutional and policy-level safeguards.
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