Human Pose Estimation for Fitness Exercise Movement Correction

Based on computer vision technology, this research suggests an application to identify and assess a fitness practitioner’s movements. Several fitness movements such as lifting weights, squat jumps, and pull-ups that are very beneficial for health and body fitness become the main movement for body building. However, those kinds of activities may be very dangerous if done incorrectly. Based on the problem, we developed an application based on computer vision to recognize and correct the pose accuracy of fitness practitioners by using input in the form of videos that record the movements of fitness practitioners continuously. To categorize the many forms of fitness sport movements, this system uses the support vector machine (SVM) method. On the monitor screen, the classification results will be visible. The result shows that the accuracy of the system is 96.87% by using SVM with the Radial Basis Function (RBF) kernel type and can make corrections to four types of fitness movements with a testing accuracy of 90.62%.