A Real-Time IoT-Based Intelligent Health Monitoring Framework with Multi-Sensor Fusion and Automated Anomaly Detection for Continuous Cardiac Assessment

Continuous and real-time health monitoring is essential for the early detection of cardiovascular and physiological abnormalities in remote and resource-limited settings. This research aims to develop an intelligent IoT-based framework for multi-sensor health data acquisition, real-time processing, and automated anomaly detection for continuous cardiac and physiological assessment. The proposed system integrates multiple physiological sensors, including heart rate, SpO₂, ECG, blood pressure, and body temperature modules, interfaced with a microcontroller-based edge computing unit for real-time signal processing and decision-making. A rule-based intelligent alert mechanism is implemented to enable automated detection of abnormal conditions and trigger immediate emergency notifications via wireless communication channels including Bluetooth and GSM. Data transmission to cloud-based platforms supports remote monitoring and longitudinal health tracking. The system architecture follows a layered IoT design encompassing sensing, processing, communication, and application layers. Performance was validated through MATLAB-based simulation and experimental testing under multiple activity conditions, achieving accuracies of 92.85% for heart rate, 98.86% for SpO₂, 98.25% for systolic blood pressure, 97.37% for diastolic blood pressure, and 99.7% for body temperature. The results demonstrate that the proposed framework provides a reliable, cost-effective, and scalable solution for continuous remote health monitoring, with significant implications for smart healthcare systems and clinical decision support in IoT environments.