Building Real-Time Movement Recognition with OpenCV

Developing real-time movement recognition for healthcare applications presents unique challenges. When building Reviva, I needed to process video input from smartphone cameras and provide instant feedback to users performing rehabilitation exercises.

The key challenge was ensuring accuracy while maintaining real-time performance. I used OpenCV for video processing and feature extraction, which allowed me to extract key points and movement patterns from the video stream.

I then trained a custom PyTorch model to classify these movements, focusing on the specific exercises needed for post-stroke rehabilitation. The model needed to be lightweight enough to run on mobile devices while still providing accurate feedback.

The result was a system that could analyze movements in real-time and provide personalized feedback, helping patients perform exercises correctly even without a physical therapist present.