Uncertainty-Aware Vital Sign Estimation for Reliable Wearable Healthcare Applications
Keywords:
wearable healthcare, uncertainty quantification, vital sign estimation, physiological monitoring, trustworthy artificial intelligence, health data governanceAbstract
Wearable healthcare systems increasingly produce continuous streams of physiological data for clinical screening, remote patient monitoring, and personal health management. However, the translation of raw sensor signals into reliable vital sign estimates remains difficult outside controlled laboratory settings because motion artifacts, sensor displacement, environmental variability, and anatomical differences introduce substantial uncertainty into the inference pipeline. Deterministic models often generate point estimates without communicating the confidence that should accompany each prediction, which can lead to overinterpretation, false alarms, or missed clinical deterioration. This paper examines uncertainty-aware vital sign estimation from a systems perspective, emphasizing architectural, infrastructural, governance, deployment, and policy considerations rather than algorithmic detail alone. It discusses how uncertainty quantification can be integrated across wearable sensing layers, edge computing nodes, clinical data platforms, and organizational workflows. The paper further explores structural trade-offs around real-time processing, energy consumption, data quality assurance, interoperability, and privacy preservation. Robustness and fairness are addressed as system properties that must be actively engineered across heterogeneous populations and use contexts. Sustainability challenges associated with device lifecycles, model drift, and continuous learning are considered alongside regulatory and ethical implications. By situating uncertainty-aware inference within the broader socio-technical infrastructure of wearable healthcare, the paper argues that reliable vital sign estimation requires coordinated design decisions spanning sensors, algorithms, data governance, and clinical integration. Future research should prioritize standardized uncertainty reporting, calibration under realistic deployment conditions, and governance mechanisms that make confidence information interpretable and actionable for patients, clinicians, and algorithmic oversight systems.
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