Graph Neural Networks for Multisensor Physiological State Modeling in Human Activity Monitoring
Keywords:
graph neural networks; multisensor fusion; physiological state modeling; human activity monitoring; wearable systems; responsible deploymentAbstract
Human activity monitoring has expanded from classifying locomotion to estimating complex physiological states from multiple wearable sensors. These sensor streams are heterogeneous, asynchronous, and physiologically coupled, which makes flat feature representations insufficient for reliable inference. Graph neural networks offer a principled relational inductive bias for modeling interacting physiological subsystems. This paper presents a systems-oriented analysis of graph neural networks for multisensor physiological state modeling in human activity monitoring. It examines the shift from Euclidean and sequence-based sensor processing to graph-based relational inference, the architectural choices that shape graph construction and message passing, and the deployment trade-offs across cloud, edge, and on-device settings. The discussion further addresses data infrastructure requirements, robustness under distribution shift, uncertainty quantification, and regulatory alignment. Fairness and governance are treated as central design constraints because physiological sensing can encode population-specific differences and sensitive health information. The paper also considers sustainability, long-term model evolution, and institutional accountability. By integrating these dimensions, the analysis moves beyond predictive performance to clarify the structural trade-offs that determine whether graph-based physiological state modeling can be deployed responsibly in wearable, clinical, and occupational environments.
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