Low-Power AI Model Optimization for Wearable Health Monitoring Applications

Wearable health monitoring devices have emerged as crucial tools for continuous tracking of vital physiological parameters such as electrocardiogram (ECG), heart rate, and oxygen saturation. With the integration of artificial intelligence (AI), these devices can provide real-time analysis and early detection of health anomalies. However, the constrained computational resources and limited battery capacity of wearable devices pose significant challenges for deploying deep learning models. This paper proposes a comprehensive framework for low-power AI model optimization tailored for wearable health monitoring applications. The framework employs quantization, pruning, and adaptive sampling to minimize computational load while maintaining high diagnostic accuracy. Experimental evaluations on public health datasets (PhysioNet, MIMIC-III) demonstrate up to 45% reduction in energy consumption with less than 2% accuracy degradation. The results highlight the potential of optimized AI models to enable longer battery life and efficient, real-time inference on wearable platforms, thus advancing the field of mobile health (mHealth) technologies.