Domain Adaptation for Cross-Device Robustness in Wearable Photoplethysmographic Health Monitoring
Keywords:
domain adaptation, wearable photoplethysmography, cross-device generalization, physiological monitoring, health equity, regulatory governanceAbstract
Wearable photoplethysmographic sensors have become central to continuous health monitoring, yet their clinical and consumer utility remains constrained by substantial variability across device types, skin tones, sensor placements, and motion conditions. Domain adaptation offers a systems-level strategy for learning physiological representations that remain stable across heterogeneous data sources. This paper examines cross-device robustness from an interdisciplinary perspective, integrating signal processing, machine learning, deployment architecture, and governance. It argues that cross-device generalization is not only an algorithmic problem but also a property of the wider socio-technical infrastructure in which wearable monitors are embedded. The analysis reviews characteristic sources of photoplethysmographic distributional shift, including optical design differences, tissue interface variability, and environmental motion artifacts. It then connects these sources to formal domain adaptation perspectives and discusses adversarial, discrepancy-based, and self-supervised methods as components of a resilient monitoring architecture. The paper further considers how data curation, fairness, regulatory oversight, and lifecycle maintenance shape the robustness of deployed systems. Robustness mechanisms must be evaluated not only under controlled benchmarks but also through continuous postmarket evidence, equity audits, and transparent uncertainty reporting. The discussion concludes that durable cross-device robustness emerges from the alignment of technical adaptation capacity with organizational governance, device certification frameworks, and participatory design practices. Future systems should treat domain adaptation as a continuous infrastructure function rather than a one-time model correction.
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