Context-Aware Physiological Signal Quality Assessment for Intelligent Wearable Healthcare Systems
Keywords:
physiological signal quality; wearable healthcare; context awareness; edge computing; machine learning; data governance; fairness; socio-technical systemsAbstract
Wearable healthcare systems increasingly rely on continuous physiological sensing to support clinical decision-making, remote monitoring, and preventive care. However, the quality of signals acquired in uncontrolled ambulatory environments is highly variable, and poor data quality can cascade into erroneous physiological estimates, inappropriate alerts, and degraded clinical trust. Context-aware signal quality assessment has emerged as a promising approach to address these challenges by interpreting physiological waveforms in relation to motion, device placement, environmental conditions, user behavior, and clinical state. This paper presents a system-level examination of context-aware physiological signal quality assessment for intelligent wearable healthcare systems. It discusses architectural trade-offs across sensing, edge, and cloud layers, emphasizing the need for adaptive quality policies rather than static thresholding. The paper further analyzes context modeling, feature engineering, computational distribution, robustness, fairness, governance, privacy, and sustainability. It argues that credible deployment requires not only accurate quality classifiers but also coherent integration with socio-technical infrastructure, clinical workflows, and regulatory frameworks. Structural tensions between specificity and generalization, energy efficiency and model complexity, and privacy and longitudinal learning are examined in depth. The discussion draws on cross-domain comparisons and clinical translation considerations to identify forward-looking design principles for wearable systems that must operate reliably across heterogeneous populations and real-world conditions. The paper concludes that context-aware signal quality assessment should be treated as an infrastructural capability rather than an isolated algorithmic module, with explicit attention to accountability, equity, and long-term sustainability.
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