IoT and Machine Learning based Smart Health Monitoring System

This project creates an intelligent health monitoring system that tracks vital health parameters such as heart rate, body temperature, and oxygen saturation in real time. Leveraging IoT technology, the system collects and transmits health data to a cloud platform, enabling remote access for patients and healthcare professionals.

Machine learning algorithms analyze the collected data to detect anomalies, predict health risks, and provide personalized health insights. The system generates instant alerts for abnormal readings, enabling timely medical interventions. A user-friendly mobile app offers live health monitoring, detailed analysis, and actionable recommendations to promote proactive healthcare management.

Key Features:

• Real-time monitoring of vital health parameters with IoT-enabled connectivity.
• Machine learning-powered health risk prediction and anomaly detection.
• Cloud-based data storage for comprehensive analysis and long-term tracking.
• Mobile app with live updates, alerts, and personalized health insights.

This project includes source code, complete hardware setup, detailed analysis, and a 200-page project report in soft copy, making it an ideal choice for students interested in IoT and AI applications in healthcare technology.