Hand Pose Estimation for Rheumatoid Arthritis
·1 min
Region Ensemble Network in PyTorch + dlib. Real-time Kinect tracking, geometric augmentation.
Technologies Used #
Deep Learning:
- PyTorch implementation
- Region Ensemble Network (REN) architecture
- Custom pose estimation model
Computer Vision:
- dlib for face and hand detection
- Kinect sensor integration
- Real-time tracking pipeline
Data Processing:
- Geometric data augmentation
- Point cloud processing
- Depth map analysis
Key Features #
Real-Time Tracking:
- Microsoft Kinect sensor integration
- Live hand pose estimation
- Low-latency processing
Region Ensemble Network:
- State-of-the-art pose estimation architecture
- Multiple region proposals
- Ensemble prediction for accuracy
Medical Application:
- Designed for rheumatoid arthritis assessment
- Hand movement tracking
- Clinical measurement support
Data Augmentation:
- Geometric transformations
- Robust to varying hand positions
- Improved model generalization
Performance:
- Real-time processing capability
- Accurate joint localization
- Depth-aware estimation
Use Cases #
- Rheumatoid arthritis assessment
- Hand mobility measurement
- Clinical research applications
- Physical therapy monitoring