Intelligent Wearable Systems for Remote Patient Monitoring Using Multimodal Biomedical Signal Analytics
Keywords:
wearable systems, remote patient monitoring, multimodal biomedical signals, signal analytics, health data governance, fairness, sustainabilityAbstract
The integration of wearable sensing, multimodal biomedical signal processing, and intelligent decision support is transforming remote patient monitoring from episodic measurement into continuous and context-aware physiological surveillance. This paper presents a system-level analysis of intelligent wearable systems for remote patient monitoring, emphasizing architectural trade-offs, multimodal data fusion, robustness, governance, equity, and sustainability. The discussion begins with the structural organization of body area networks, edge computing, and cloud-based clinical services, focusing on the need for adaptive allocation of processing across layers to balance latency, energy consumption, privacy, and diagnostic quality. It then examines core physiological modalities, including photoplethysmography, electrocardiography, accelerometry, respiratory effort, and electrodermal activity, and their integration through feature-level and decision-level analytics. A recurrent theme is that multimodal systems are more resilient to motion artifact and missing data than single-modal monitors, but only when signal quality estimation, synchronization, and context-aware inference are treated as first-class infrastructure. The paper further addresses governance and fairness challenges, including data minimization, federated learning, algorithmic bias, and regulatory compliance. It argues that sustainable clinical deployment requires not only signal processing accuracy but also attention to device lifecycle, energy consumption, interoperability, workflow integration, and socio-technical equity. Future directions highlight the need for auditable adaptive systems, standardized multimodal data models, and policy frameworks that ensure intelligent monitoring is safe, fair, and robust across heterogeneous populations and care settings.
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