Computer Vision and Photoplethysmography Fusion for Contactless and Wearable Health Assessment
Keywords:
computer vision; photoplethysmography; multimodal fusion; wearable health; remote physiological sensing; health data governanceAbstract
The convergence of computer vision and wearable photoplethysmography has created new opportunities for continuous, noninvasive health assessment outside clinical settings. This paper presents a system-level examination of multimodal fusion architectures that combine contactless facial video analysis with wearable optical pulse sensing. Rather than focusing narrowly on algorithmic performance, the discussion emphasizes structural trade-offs across sensing, synchronization, edge computing, data fusion, and clinical deployment. The analysis addresses key architectural decisions, including data-level, feature-level, and decision-level integration, and considers how these choices affect robustness, interpretability, latency, energy consumption, and privacy. The paper further examines challenges arising from uncontrolled environments, motion artifacts, illumination variation, skin tone heterogeneity, and sensor drift. Governance and fairness concerns are treated as first-order system requirements rather than secondary compliance issues. The regulatory landscape surrounding biometric and health data, including the General Data Protection Regulation and emerging artificial intelligence governance frameworks, is interpreted in relation to continuous physiological monitoring. Deployment and sustainability are evaluated in terms of interoperability with existing health infrastructures, device lifecycle management, digital equity, and environmental footprint. The paper argues that sustainable health assessment systems must balance contactless coverage and wearable continuity through adaptive fusion strategies that are simultaneously robust, fair, and governed by transparent socio-technical policies.
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