Adaptive Sensor Fusion for Real-Time Detection of Exercise-Induced Cardiovascular Responses
Keywords:
adaptive sensor fusion; cardiovascular monitoring; wearable sensors; physiological signal processing; real-time inference; health governance; system architectureAbstract
Exercise-induced cardiovascular responses are inherently dynamic, heterogeneous, and subject to considerable interindividual variability. Real-time detection of these responses through wearable sensing requires not only robust physiological signal acquisition but also adaptive fusion mechanisms capable of reconciling noisy, asynchronous, and context-dependent data streams. This paper presents a system-level analysis of adaptive sensor fusion for the continuous assessment of cardiovascular responses during physical activity. The discussion addresses architectural design choices, including centralized, distributed, and hybrid fusion topologies, and examines the trade-offs among latency, interpretability, energy consumption, and resilience to motion artifacts. A central argument is that real-time cardiovascular monitoring should be treated as a socio-technical infrastructure rather than a narrowly defined signal processing problem. Consequently, the paper integrates perspectives from systems engineering, artificial intelligence, biomedical signal processing, and governance. It evaluates data acquisition, preprocessing, feature extraction, adaptive inference, uncertainty management, and deployment in real-world settings. The analysis further considers fairness, privacy, regulatory compliance, and long-term sustainability as first-class design constraints. By connecting sensor fusion architectures with infrastructure governance and policy implications, the paper offers a forward-looking research agenda for dependable, equitable, and scalable cardiovascular monitoring systems. The proposed framing emphasizes that adaptive fusion is not merely a computational optimization task but an ongoing orchestration of sensing resources, learning processes, and institutional oversight in the presence of physiological and environmental uncertainty.
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