Cross-Device Transfer Learning for Reliable Vital Sign Estimation in Wearable Healthcare Systems
Keywords:
cross-device transfer learning; wearable healthcare; vital sign estimation; domain adaptation; physiological monitoring; system reliabilityAbstract
Wearable healthcare systems increasingly rely on photoplethysmographic and electrocardiographic signals for continuous vital sign estimation, but discrepancies across device models, sensor placements, and usage contexts cause distribution shifts that degrade model performance. This paper examines cross-device transfer learning as a system-level strategy for achieving reliable estimation of heart rate, respiration rate, blood oxygen saturation, and related physiological parameters. The work does not claim a single algorithmic breakthrough; instead it analyzes the structural trade-offs involved when transfer learning is embedded within wearable monitoring infrastructures. The paper discusses domain shift sources including sensor hardware differences, optical path variations, motion artifact profiles, and device firmware heterogeneity. It then presents an architectural perspective on shared representation learning, adversarial domain alignment, fine-tuning, and calibration, with attention to edge-cloud partitioning and model governance. The paper evaluates implications for data infrastructure, privacy, fairness, robustness, energy consumption, clinical validation, and regulatory compliance. A central argument is that reliability must be engineered as an infrastructure property, not as a property of isolated models. Cross-device transfer learning can improve generalization and reduce reliance on per-device labeled data, but only when integrated with continuous monitoring, uncertainty estimation, federated data governance, and postmarket surveillance. The paper concludes with forward-looking policy and deployment recommendations for sustainable wearable healthcare systems.
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