Pose Trainer: Correcting Exercise Posture using Pose Estimation
This addresses the issue of ineffective and potentially dangerous fitness exercises for users by offering real-time correction, though it is incremental as it builds on existing pose estimation methods.
The paper tackles the problem of incorrect exercise posture by introducing Pose Trainer, an application that uses pose estimation to detect and provide personalized feedback on user form, achieving functionality on four common exercises with a dataset of over 100 videos.
Fitness exercises are very beneficial to personal health and fitness; however, they can also be ineffective and potentially dangerous if performed incorrectly by the user. Exercise mistakes are made when the user does not use the proper form, or pose. In our work, we introduce Pose Trainer, an application that detects the user's exercise pose and provides personalized, detailed recommendations on how the user can improve their form. Pose Trainer uses the state of the art in pose estimation to detect a user's pose, then evaluates the vector geometry of the pose through an exercise to provide useful feedback. We record a dataset of over 100 exercise videos of correct and incorrect form, based on personal training guidelines, and build geometric-heuristic and machine learning algorithms for evaluation. Pose Trainer works on four common exercises and supports any Windows or Linux computer with a GPU.