Smart Health Monitoring System Using IoT and Artificial Intelligence

Abstract

The Smart Health Monitoring System is an intelligent healthcare platform designed to provide real-time monitoring and analysis of patient health conditions using IoT, Machine Learning, Web Technology, Mobile Application Development, and R Programming. The system integrates the MAX30102 sensor with ESP32 to monitor vital parameters such as heart rate and blood oxygen saturation (SpO₂) levels continuously. The collected sensor data is transmitted to cloud-connected web and mobile applications for real-time visualization and storage. Python FastAPI is used as the backend framework for API communication and machine learning integration, while Firebase services provide authentication and cloud synchronization. Machine Learning algorithms are implemented to analyze patient health patterns and detect abnormal health conditions with prediction accuracy between 90–92%. R Programming is integrated for healthcare analytics and visualization of patient reports and trends. Experimental evaluation demonstrates stable real-time monitoring with approximately 95% sensor accuracy and average system response latency of 2–3 seconds. The proposed system contributes toward intelligent and accessible healthcare by enabling efficient, portable, and user-friendly health monitoring.

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