SentinelCare is an AI-powered fall detection system that utilizes pose estimation and machine learning to detect falls and alert caregivers in real time.
It is designed for healthcare facilities, elderly homes, and smart surveillance systems, focusing on safety, privacy, and response efficiency.
- 🚨 Real-Time Fall Detection – Detects abnormal human postures through pose estimation.
- 🎯 High Accuracy – Powered by cutting-edge models (OpenPose, BlazePose, MediaPipe).
- 🧩 Edge & Cloud Compatible – Deploy on Raspberry Pi, Jetson Nano, or cloud servers.
- 📢 Smart Alerts – Notifies via Email, SMS, or IoT (MQTT, Firebase).
- ⚙️ Adjustable Sensitivity – Customize thresholds to reduce false positives.
- 🔒 Privacy-Preserving – Uses skeletal keypoints instead of raw video.
| Category | Tools & Frameworks |
|---|---|
| Language | Python |
| Deep Learning | TensorFlow, PyTorch |
| Pose Estimation | OpenPose, BlazePose, MediaPipe |
| Computer Vision | OpenCV |
| Web Frameworks | Flask, FastAPI |
| Messaging / IoT | MQTT, Firebase, Twilio |
| Data Processing | NumPy, Pandas |
The SentinelCare architecture processes live or recorded video streams through pose estimation models and intelligent fall detection algorithms, followed by alert dispatch via IoT or messaging services.
- Python ≥ 3.8
- (Optional) Virtual environment
# Clone repository
git clone https://github.com/Dr-irshad/SentinelCare-Advanced-AI-Fall-Detection-System.git
cd SentinelCare-Advanced-AI-Fall-Detection-System
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txtYou can train or test the system using publicly available datasets:
- Convert models using TensorFlow Lite or ONNX for lightweight performance.
- Optimize with OpenVINO on Intel-based systems.
- Deploy via AWS Lambda, Google Cloud Functions, or Azure Functions.
- Integrate with cloud storage and IoT alert systems.
| Type | Integration |
|---|---|
| SMTP configuration | |
| SMS | Twilio API |
| IoT / Messaging | MQTT, Firebase, or WebSockets |
- 🧬 Deep learning–based fall classification (LSTM / Transformer)
- 🎥 Multi-camera coordination for larger coverage
- ⌚ Integration with wearable IMU sensors
- 📊 Cloud dashboard with real-time analytics and logs
Contributions are welcome!
To contribute:
- Fork this repository
- Create a feature branch (
feature/your-feature) - Commit your changes
- Open a Pull Request
For discussions or suggestions, please open an issue.
This repository provides architectural and research documentation only.
All proprietary code developed under FLAIR remains confidential.
Shared content is for educational and research purposes only.
SentinelCare is a collaborative AI research initiative exploring real-time human activity recognition for safety monitoring.
Developed with expertise in computer vision, deep learning, and IoT-based alert systems.This repository showcases the design architecture, methodology, and deployment framework — not proprietary implementation code.