Tiny ML-Enabled Edge Computing for Autonomous Health Monitoring and Anomaly Detection
DOI:
https://doi.org/10.15662/IJEETR.2026.0802069Keywords:
TinyML, Edge Computing, Anomaly Detection, Wearable Health Systems, Autonomous Alerting, Geriatric Care, Microcontroller InferenceAbstract
This paper presents a decentralized, AI-powered wearable system for real-time physiological monitoring and anomaly detection. By leveraging TinyML (machine learning on resource-constrained edge devices), the proposed system eliminates latency and dependency on cloud-based healthcare solutions. The architecture integrates a multi-sensor array capturing both hemodynamic and kinematic data with an ultra-low-power microcontroller to perform on-device inference for cardiac anomalies and fall detection
A key contribution is the integration of a hybrid solar-battery energy model, ensuring sustainable operation in resource-limited environments. Experimental results demonstrate that edge-based intelligence significantly reduces emergency response time through autonomous, multi-channel alert mechanisms. This work contributes to accessible and reliable healthcare by enabling high-accuracy, offline diagnostic support for geriatric and remote patient monitoring.
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