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Repetitive counting (RepCount) is critical in various applications, such asfitness tracking and rehabilitation. Previous methods have relied on theestimation of red-green-and-blue (RGB) frames and body pose landmarks toidentify the number of action repetitions, but these methods suffer from anumber of issues, including the inability to stably handle changes in cameraviewpoints, over-counting, under-counting, difficulty in distinguishing betweensub-actions, inaccuracy in recognizing salient poses, etc. In this paper, basedon the work done by [1], we integrate joint angles with body pose landmarks toaddress these challenges and achieve better results than the state-of-the-artRepCount methods, with a Mean Absolute Error (MAE) of 0.211 and an Off-By-One(OBO) counting accuracy of 0.599 on the RepCount data set [2]. Comprehensiveexperimental results demonstrate the effectiveness and robustness of ourmethod.

Advancements in Repetitive Action Counting: Joint-Based PoseRAC Model With Improved Performance