  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.
