Graph Neural Network-Based Modeling of Multisensor Physiological Interactions During Physical Activity
Keywords:
graph neural networks, multisensor physiological interactions, physical activity monitoring, wearable systems, edge intelligence, health data governanceAbstract
Continuous monitoring of physiological dynamics during physical activity has become central to precision health, athletic performance, and remote rehabilitation. However, conventional multisensor fusion models often treat sensor channels as independent streams or grid-like inputs, thereby ignoring the relational and time-varying interactions among cardiac, respiratory, electrodermal, and motion-related modalities. Graph neural networks provide a principled framework for representing these multisensor physiological interactions as graph-structured data, enabling learned relational inference under motion artifacts and nonstationary conditions. This paper develops a system-level perspective on graph neural network-based modeling of multisensor physiological interactions during physical activity. It examines architectural arrangements spanning wearable acquisition, edge preprocessing, graph construction, inference, and closed-loop feedback. The discussion foregrounds structural trade-offs among centralized and distributed processing, model expressiveness and interpretability, energy efficiency, communication overhead, and resilience to missing or degraded sensor data. Attention is also given to governance, fairness, privacy, standardization, and policy implications in deployments across heterogeneous populations and regulatory contexts. Rather than focusing on specific equations or training procedures, the paper articulates how system design decisions shape the viability and trustworthiness of graph-based physiological monitoring. Case illustrations from wearable activity recognition, remote patient monitoring, and ambient assisted living are used to clarify deployment considerations. The paper concludes with forward-looking perspectives on sustainable, accountable, and adaptive graph-based multisensor infrastructures.
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