Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region
This work addresses the need for automated feedback to reduce injury risk for novices in aerobic exercises, but it is incremental as it builds on existing background subtraction and AR techniques.
The paper tackles the problem of assessing aerobic routine performance by proposing an image similarity measure using intersected image regions, achieving an accuracy of 93.67% on a limited dataset.
It is recommended for a novice to engage a trained and experience person, i.e., a coach before starting an unfamiliar aerobic or weight routine. The coach's task is to provide real-time feedbacks to ensure that the routine is performed in a correct manner. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method is implemented inside an Augmented Reality (AR) desktop app that employs a single RGB camera to capture still images of the subject as he or she progresses through the routine. The background-subtracted body pose image is compared against the exemplar body pose image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function is reported to have an accuracy of 93.67%.