AI-Based Health Monitoring System

Abstract: Continuous and real-time health monitoring has emerged as one of the most pressing challenges in modern healthcare, particularly in the context of ageing populations, rising prevalence of chronic diseases, and the growing demand for remote patient care. This paper presents the design and implementation of an Artificial Intelligence (AI)- based health monitoring system capable of acquiring, processing, and analysing multiple physiological parameters — including heart rate, blood oxygen saturation (SpO2), body temperature, and blood pressure — in real time. The proposed system integrates low-cost wearable sensors with a microcontroller-driven edge computing unit and a cloud-connected dashboard, forming an end-to-end pipeline from data acquisition to clinical decision support. A hybrid machine learning model combining a Long Short-Term Memory (LSTM) network for temporal pattern recognition with a Random Forest classifier for anomaly labelling is trained on publicly available physiological datasets. The model achieves a classification accuracy of 94.7% in detecting critical health events such as tachycardia, hypoxia, and hypertensive episodes. Automated alert notifications are dispatched to caregivers and physicians whenever abnormal readings are detected, enabling timely intervention. Experimental evaluation demonstrates that the system maintains end-to-end latency below 1.8 seconds and sustains reliable operation across a 24-hour continuous monitoring window. The results confirm that integrating AI with wearable sensing technology offers a scalable and cost-effective approach to preventive healthcare.

Keywords: Artificial Intelligence, Health Monitoring, Wearable Sensors, LSTM, Random Forest, IoT in Healthcare, Remote Patient Monitoring, Anomaly Detection, Edge Computing, Physiological Signals.