Personalized Physiological Monitoring through Domain Adaptation of Wearable Sensor Models
Keywords:
domain adaptation; wearable sensors; physiological monitoring; personalized health; fairness; data governance; deployment infrastructureAbstract
Continuous physiological monitoring through wearable devices promises more personalized and timely care, but sensor-level heterogeneity, individual variability, skin tone differences, motion artifacts, and deployment conditions create domain shifts that degrade model generalization. Domain adaptation offers a framework for aligning feature representations and predictive models across source and target distributions without requiring extensive labeled data from each individual. This paper examines the system-level challenges and structural trade-offs in designing personalized physiological monitoring systems that rely on domain adaptation of wearable sensor models. It addresses architectural choices for shared feature encoders, adversarial and discrepancy-based alignment, personalization layers, and real-time inference constraints. The discussion extends to data governance, privacy, fairness, robustness, and infrastructure sustainability. The paper emphasizes that effective deployment requires balancing domain adaptation accuracy against computational overhead, regulatory transparency, cross-device interoperability, and long-term model maintenance. Conceptual case illustrations drawn from ambulatory cardiac monitoring, cuffless blood pressure estimation, and activity-aware vital sign extraction are used to illustrate trade-offs. The analysis suggests that adaptive personalization should be treated as a continuous system process involving model retraining, deployment infrastructure, and policy oversight rather than an isolated machine learning technique. The paper concludes with forward-looking perspectives on federated personalization, auditability, and sustainability in wearable health systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
