ElderCare: AI-Driven Fall Detection & Health Monitoring

ElderCare is an intelligent monitoring system designed to enhance the safety and well-being of elderly individuals through real-time fall detection and health monitoring.

🚀 Key Features

• Real-time Fall Detection

  • Utilizes OpenCV and MediaPipe for accurate posture analysis
  • Instant alerts for detected falls
  • Continuous monitoring of movement patterns

• Wearable Health Monitoring

  • Arduino-based prototype for vital signs tracking
  • Heart rate and pulse monitoring
  • Body temperature sensing

• Emergency Alert System

  • Immediate notifications to caregivers
  • Quick response protocol activation

🛠️ Tech Stack

• Backend: Python 3.8+
• Computer Vision: OpenCV, MediaPipe
• Hardware: Arduino
• Data Handling: JSON

⚙️ Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ElderCare.git
   cd ElderCare

2. Install dependencies:

   pip install -r requirements.txt

3. Connect your Arduino device and update the configuration in config.json.

🚀 Quick Start

1. Start the monitoring system:

   python main.py

2. For fall detection:

   python fall_detection.py

📱 Demo

To see the system in action:

1. Ensure your camera is connected
2. Run the fall detection script
3. The system will display real-time analysis

📈 Future Scope

• Integration with mobile applications for remote monitoring
• Advanced health analytics and predictive modeling
• Expanded sensor support for comprehensive health tracking

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines before submitting pull requests.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• MediaPipe for the pose estimation models
• OpenCV community for computer vision tools
• Arduino for the hardware platform