Multimodal Sensor Fusion for Robust Physiological Monitoring during Free-Living Activities
Keywords:
physiological monitoring, multimodal sensor fusion, wearable systems, free-living activities, robustness, data governance, fairness, edge computingAbstract
The expansion of wearable sensing technologies has created new opportunities for continuous physiological monitoring beyond controlled clinical environments. However, free-living conditions introduce substantial challenges related to motion artifacts, contextual variability, heterogeneous sensor placements, environmental interference, and population diversity. Single-modality physiological sensing often fails to provide sufficiently robust estimates during unstructured daily activities. Multimodal sensor fusion offers a promising system-level strategy by combining complementary physiological, kinematic, and environmental data streams to improve estimation reliability, contextual awareness, and clinical interpretability. This paper presents a long-form analysis of multimodal sensor fusion for physiological monitoring during free-living activities. It examines the architectural dimensions of sensing, edge processing, communication, and cloud analytics while emphasizing structural trade-offs among accuracy, energy consumption, latency, privacy, and scalability. The discussion explores early, intermediate, and decision-level fusion approaches and their implications for system robustness. It further considers context-aware adaptation, motion artifact mitigation, data governance, algorithmic fairness, population heterogeneity, and deployment sustainability. Rather than focusing narrowly on signal-level algorithms, the paper adopts a systems-oriented perspective, integrating concerns from artificial intelligence, wearable engineering, socio-technical infrastructure, and health policy. The analysis highlights the need for governance frameworks that address continuous physiological data collection and emphasizes the importance of fairness across demographic groups. Forward-looking perspectives on edge intelligence, federated learning, digital biomarkers, and sustainable wearable ecosystems are provided. The paper argues that robust free-living physiological monitoring requires not only advanced fusion algorithms but also carefully designed sociotechnical architectures that align technical performance with ethical and regulatory requirements.
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