Energy-Efficient IoT and Edge Computing Framework for Wearable Health Monitoring and Chronic Disease Management

Abstract

Advances in Internet of Things (IoT), edge computing, and smart sensor technologies are reshaping chronic disease management and remote patient care, with growing emphasis on energy-efficient solutions. This work presents an energy-efficient wearable health monitoring framework that integrates IoT-enabled smart sensors with edge-based data processing to continuously capture and analyze vital parameters, including heart rate, blood pressure, blood glucose, and SpO₂ levels. By performing preliminary data processing and abnormality detection at the edge, the system reduces cloud communication overhead, thereby extending device battery life and improving responsiveness. Machine learning algorithms embedded in the edge layer detect abnormal physiological patterns and predict potential health risks, triggering early alerts for timely medical intervention. Experimental evaluation demonstrates high accuracy (95.4%), sensitivity (93.8%), and specificity (96.2%) in detecting and monitoring chronic conditions. Real-time data visualization and personalized health insights further empower patients to take proactive roles in managing their health, while healthcare providers benefit from reduced hospital visits and improved continuity of care. The proposed framework addresses key challenges in wearable healthcare systems, particularly power optimization and data security, making it a cost-effective and scalable solution for sustainable, connected healthcare ecosystems.