Video Demo: https://www.youtube.com/shorts/O5sprqx_5Xk

#PoseVision What it does: This project analyzes squat videos to detect imbalances in knee angles, which can indicate potential issues with form. When the angles of the knees are different, the system highlights this in real-time, drawing attention to areas that may lead to injury. By providing clear visual feedback, it helps users identify posture concerns and improve their squatting technique.

How we built it: We built this project using Python and Flask for the back-end, leveraging OpenCV and MediaPipe for real-time video analysis. MediaPipe's pose estimation model helps us detect key joint positions, while OpenCV processes and highlights knee angle discrepancies. The front-end is powered by HTML and CSS, providing a clean and user-friendly interface for video upload and feedback display.

Link to Devpost for visuals: https://devpost.com/software/squatvision