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Physical exercise is an essential component of rehabilitation programs thatimprove quality of life and reduce mortality and re-hospitalization rates. InAI-driven virtual rehabilitation programs, patients complete their exercisesindependently at home, while AI algorithms analyze the exercise data to providefeedback to patients and report their progress to clinicians. To analyzeexercise data, the first step is to segment it into consecutive repetitions.There has been a significant amount of research performed on segmenting andcounting the repetitive activities of healthy individuals using raw video data,which raises concerns regarding privacy and is computationally intensive.Previous research on patients' rehabilitation exercise segmentation relied ondata collected by multiple wearable sensors, which are difficult to use at homeby rehabilitation patients. Compared to healthy individuals, segmenting andcounting exercise repetitions in patients is more challenging because of theirregular repetition duration and the variation between repetitions. This paperpresents a novel approach for segmenting and counting the repetitions ofrehabilitation exercises performed by patients, based on their skeletal bodyjoints. Skeletal body joints can be acquired through depth cameras or computervision techniques applied to RGB videos of patients. Various sequential neuralnetworks are designed to analyze the sequences of skeletal body joints andperform repetition segmentation and counting. Extensive experiments on threepublicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, andIntelliRehabDS, demonstrate the superiority of the proposed method compared toprevious methods. The proposed method enables accurate exercise analysis whilepreserving privacy, facilitating the effective delivery of virtualrehabilitation programs.

Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints