Non-Invasive Fall Detection and Health Monitoring System for Elderly Care

This project develops a non-invasive fall detection and health monitoring system specifically designed for elderly care. Utilizing IoT technology, the system continuously monitors the health parameters of elderly individuals, such as heart rate, body temperature, and movement, transmitting real-time data to a cloud platform for remote access.

Machine learning algorithms process the data to detect fall events and abnormal health conditions, issuing instant alerts to caregivers or family members for immediate action. The system also predicts potential health risks and provides insights for improving the well-being of elderly individuals. This system is ideal for caregivers, healthcare professionals, and families seeking to ensure the safety and health of elderly individuals at home or in care facilities.

A user-friendly mobile app provides live health updates, fall detection notifications, and historical data visualization, enabling caregivers to monitor health conditions remotely and efficiently.

Key Features:

• Continuous monitoring of health parameters and fall detection with IoT-enabled devices.
• ML-powered fall detection, abnormal health condition alerts, and predictive health insights.
• Cloud-based data storage for long-term health tracking and reporting.
• Mobile app with live updates, alerts, and health insights tailored to elderly care.

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