Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

Abstract

It is recommended for a novice to engage a trained and experienced 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%.

Who danced better? ranked tiktok dance video dataset and pairwise action quality assessment method

“…Their method uses a novel rank-aware loss function to perform pairwise ranking of egocentric videos. John et al [26] measure the percentage of overlapping area between aligned foreground images from frames belonging to an unlabeled video and a video with an annotated score. The resulting value indicates how similar the aerobic dance moves are between the pair of videos.…”

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

“…(2) for precise analysis of athletic performance, since it has no physical effect on the athlete as in Tamas et al [347] for rowing motion and in John et al [173] for aerobic routines; and (3) for surveillance as in Bastos [29] for surfers activities. 5.…”

“…Background subtraction is also used for visual analysis of human activities like in sport (1) when important decisions need to be made quickly, (2) for precise analysis of athletic performance, and (3) for surveillance in dangerous activities. For example, John et al [173] provide a system that allow a coach to obtain real-time feedbacks to ensure that the routine is performed in a correct manner. During the initialisation stage, the stationary camera captures the background image without the user, and then each current image is subtracted to obtain the silhouette.…”