Non-Standard Squat Posture Detection Method Using Human Skeleton

Squats are essential for assessing lower limb strength. However, performing them incorrectly without professional guidance often leads to sports injuries. Currently, most detection methods rely heavily on deep neural networks and massive datasets. This approach brings several downsides. It involves high data labeling costs and heavy computing demands. It is also difficult to achieve low-latency feedback on mobile devices. Furthermore, these models often lack robustness when dealing with individual body differences. To tackle these issues, we propose a new real-time squat detection method. Our approach is built on prior rules and statistical models. Here is how it works. First, we use MediaPipe to track the body’s skeleton joints in real-time from video feeds, calculating the hip and knee angles frame by frame. Next, we build a hip-knee coordination model using linear regression. This step helps us measure how these joints move together dynamically. Finally, we verify the squat depth using a geometry-based tolerance mechanism. This feature accounts for measurement noise and natural body variations, allowing us to accurately judge if the overall posture is standard. We tested our approach on three different squat styles. The results show that our method catches improper forms quickly and efficiently in real time, achieving an accuracy of 90%.