Enhancing Remote Patient Monitoring through IoT: A Wearable Sensor Fusion Approach for Cardiovascular Health Management

Remote Case monitoring (RPM) via the Internet of Effects (IoT) bias has surfaced as a promising approach for managing cardiovascular health. This study presents a new methodology using wearable detector emulsion to enhance RPM effectiveness in cardiovascular health operation. Our approach integrates data from multiple wearable detectors, such as ECG, PPG, and accelerometer, to give comprehensive real-time monitoring of vital signs and physical exertion. Using machine literacy algorithms, the fused detector data is anatomized to descry anomalies, prognosticate cardiovascular events, and epitomize intervention strategies. Likewise, the IoT structure enables flawless communication between cases, healthcare providers, and pall-grounded PPG (Photoplethysm analytics platforms, easing timely intervention and remote discussion. The proposed frame aims to ameliorate patient issues by enabling early discovery of cardiovascular issues, optimizing treatment plans, and promoting visionary healthcare operation. Through simulation studies and confirmation with clinical data, we demonstrate the feasibility and efficacy of our wearable detector emulsion approach in enhancing RPM for cardiovascular health operation.