Viewpoint-Invariant Exercise Repetition Counting
This work addresses the need for accurate, viewpoint-invariant repetition counting in remote rehabilitation and exercise training, offering a practical solution for large-scale applications with a single camera.
The paper tackles the problem of counting exercise repetitions from video, especially for concurrent motions, by proposing a vision-based method using skeleton data. It achieves a mean absolute error of 0.06 and off-by-one accuracy up to 0.95 on standard datasets, demonstrating robust performance across various camera viewpoints.
Counting the repetition of human exercise and physical rehabilitation is a common task in rehabilitation and exercise training. The existing vision-based repetition counting methods less emphasize the concurrent motions in the same video. This work presents a vision-based human motion repetition counting applicable to counting concurrent motions through the skeleton location extracted from various pose estimation methods. The presented method was validated on the University of Idaho Physical Rehabilitation Movements Data Set (UI-PRMD), and MM-fit dataset. The overall mean absolute error (MAE) for mm-fit was 0.06 with off-by-one Accuracy (OBOA) 0.94. Overall MAE for UI-PRMD dataset was 0.06 with OBOA 0.95. We have also tested the performance in a variety of camera locations and concurrent motions with conveniently collected video with overall MAE 0.06 and OBOA 0.88. The proposed method provides a view-angle and motion agnostic concurrent motion counting. This method can potentially use in large-scale remote rehabilitation and exercise training with only one camera.