https://github.com/kichuman28/wisecare-fall_detection_model
WiseCare Fall Detection System is an innovative solution designed to enhance elderly care and safety monitoring. Using advanced computer vision and machine learning techniques, our system provides real-time fall detection capabilities, helping caregivers respond quickly to potential emergencies.
https://github.com/kichuman28/wisecare-fall_detection_model
docker flask tensorflow yolov11
Last synced: 2 months ago
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WiseCare Fall Detection System is an innovative solution designed to enhance elderly care and safety monitoring. Using advanced computer vision and machine learning techniques, our system provides real-time fall detection capabilities, helping caregivers respond quickly to potential emergencies.
- Host: GitHub
- URL: https://github.com/kichuman28/wisecare-fall_detection_model
- Owner: kichuman28
- Created: 2025-02-24T16:12:56.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-24T18:12:35.000Z (8 months ago)
- Last Synced: 2025-04-01T22:24:34.312Z (6 months ago)
- Topics: docker, flask, tensorflow, yolov11
- Language: Python
- Homepage:
- Size: 17.6 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎯 WiseCare - Fall Detection System
[](https://www.python.org/)
[](https://opencv.org/)
[](https://tensorflow.org/)
[](LICENSE)
## 🌟 Overview
WiseCare Fall Detection System is an innovative solution designed to enhance elderly care and safety monitoring. Using advanced computer vision and machine learning techniques, our system provides real-time fall detection capabilities, helping caregivers respond quickly to potential emergencies.
## 🎥 Demo Videos
### Normal Activity Detection
https://github.com/user-attachments/assets/05fe96a0-6768-4b43-b352-2e55a2f669e3
*Video showing normal activities being correctly identified*
### Fall Detection in Action
https://github.com/user-attachments/assets/b85a9054-84de-4a9e-a6ca-461a931f6cdc
*Video demonstrating successful fall detection scenarios*
## ✨ Key Features
- **Real-time Fall Detection**: Instant identification of fall events using advanced computer vision
- **High Accuracy**: Sophisticated machine learning model trained on diverse scenarios
- **Low False Positives**: Intelligent algorithm to differentiate between actual falls and normal movements
- **Privacy-Focused**: All processing done locally, ensuring user privacy
- **Easy Integration**: Simple setup process for various monitoring systems
## 🛠️ Technical Stack
- **Computer Vision**: OpenCV for real-time video processing
- **Machine Learning**: TensorFlow for fall detection model
- **Video Processing**: Advanced frame analysis and motion detection
- **Alert System**: Immediate notification system for detected falls
## 📋 Prerequisites
- Python 3.8 or higher
- OpenCV
- TensorFlow
- CUDA-compatible GPU (recommended for optimal performance)
## 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/Abelboby/wisecare-fall_detection_model.git
cd wisecare-fall_detection_model
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the application:
```bash
python main.py
```
## 📊 Performance Metrics
- Detection Accuracy: >95%
- Response Time: <1 second
- False Positive Rate: <2%
## 🤝 Contributing
We welcome contributions! Please feel free to submit a Pull Request.
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 👥 Team
- Project Lead: [Parthav Povil]
- Contributors: [Abel Boby, Adwaith Jayasankar]
## 📞 Support
For support, please open an issue in the GitHub repository or contact [support email].
---
Made with ❤️ by WiseCare Team