Remote Patient Monitoring Systems Using AI-Enabled Wearables
Abstract
The integration of artificial intelligence (AI) with wearable devices for remote patient monitoring (RPM) systems is revolutionizing healthcare, offering real-time personalized monitoring beyond the clinical setting. Wearable technologies, such as smartwatches, ECG patches, glucose monitors, pulse oximeters, and activity trackers, collect physiological data for analysis and clinical decision-making. These systems enable early disease detection, reduce hospital visits, improve chronic disease management, and enhance patient engagement. This chapter discusses the architecture, technologies, and applications of AI-based RPM systems, focusing on anomaly detection, arrhythmia recognition, glucose forecasting, sleep analysis, and health risk prediction. It also explores IoT, edge computing, TinyML, and secure communication, while examining healthcare applications, challenges, and emerging trends including multimodal AI, explainable AI, federated learning, and digital twin-assisted healthcare.
