Edge AI for Real-Time Health Monitoring with Resource-Constrained Wearable Sensors

Abstract

The proliferation of wearable physiological sensors has created opportunities for continuous, real-time health monitoring outside clinical settings. However, wearable devices operate under severe computational, memory, energy, and communication constraints, which complicate the deployment of advanced machine learning models. Edge artificial intelligence, in which inference and learning tasks are distributed across wearable nodes, edge gateways, and cloud infrastructure, offers a promising architectural response to these limitations. This article examines the system-level design of edge AI for real-time health monitoring with resource-constrained wearable sensors. It discusses architectural decomposition, data acquisition, signal reliability, model compression, communication intermittency, energy management, robustness, fairness, clinical validation, governance, and long-term sustainability. A central theme is that local inference reduces data exposure and latency but creates new trade-offs involving model accuracy, device lifetime, update cadence, and equity. The design space cannot be reduced to a single optimization target; instead, it requires coordinated decisions across hardware, model architecture, network protocols, and institutional governance. The analysis integrates perspectives from edge computing, mobile health, federated learning, algorithmic bias, and data protection. It argues that clinically credible and socially responsible deployments depend on longitudinal evidence, transparent operational monitoring, and participatory governance mechanisms. Future systems must combine compression-aware training, adaptive duty cycling, and federated personalization while preserving auditability and fairness across heterogeneous populations.