Edge AI-Based Real-Time Physiological Monitoring for Resource-Constrained Wearable Healthcare Systems
Keywords:
edge artificial intelligence; wearable healthcare; physiological monitoring; resource constraints; real-time inference; federated learning; governance; sustainabilityAbstract
The proliferation of wearable physiological sensors has created new possibilities for continuous health surveillance outside clinical settings. However, cloud-centric processing architectures introduce latency, communication bottlenecks, privacy risks, and energy overheads that are incompatible with real-time intervention. This paper examines the system-level design of edge artificial intelligence for wearable healthcare, focusing on resource-constrained devices that must acquire, process, and interpret photoplethysmography, electrocardiography, electrodermal activity, accelerometry, and related signals. We analyze architectural patterns that distribute inference across sensors, microcontrollers, gateways, and hospital information infrastructures. The discussion emphasizes hardware-software co-optimization, model compression, sensor fusion, context awareness, and the trade-offs among accuracy, latency, battery life, and clinical reliability. Attention-based methods for motion-tolerant optical sensing are considered within a broader framework of multimodal signal restoration. The paper further addresses robustness, fairness, and governance, highlighting the need for regulatory alignment, representative training data, auditability, and transparent deployment. Finally, sustainability and lifecycle concerns are discussed, including device longevity, energy footprint, and responsible update mechanisms. The argument is that real-time edge AI in wearable healthcare is not solely a machine learning problem but a socio-technical infrastructure challenge requiring coordinated advances in architecture, policy, and clinical validation.
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