IOT BASED HEALTH MONITORING OBSERVATION & NOTIFICATION USING AI

Abstract

The integration of Internet of Things (IoT) with artificial intelligence has revolutionized healthcare delivery by enabling continuous patient monitoring and early disease detection. This research presents a comprehensive IoT-based health monitoring system that combines wearable sensors with AI algorithms to observe vital parameters and generate intelligent notifications for patients and healthcare providers. The proposed system captures real-time physiological data including heart rate, blood pressure, body temperature, and oxygen saturation through interconnected IoT devices. Machine learning algorithms analyze these continuous data streams to identify abnormal patterns and predict potential health complications before they become critical. Our implementation involved 180 participants monitored over six months, with the system successfully detecting 94% of abnormal health events an average of 47 minutes before clinical symptoms manifested. The AI notification engine employs decision tree classifiers and neural networks to categorize health alerts into critical, moderate, and routine categories, reducing false alarms by 68% compared to threshold-based systems. The architecture integrates cloud computing for data storage and processing, enabling remote monitoring and telemedicine applications. Results demonstrate significant improvements in early intervention rates, with 82% of critical events receiving medical attention within 15 minutes of system alert. This work contributes a scalable framework for intelligent health monitoring suitable for chronic disease management, elderly care, and post-operative patient surveillance.