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A webcam-based machine learning approach for the three-dimensional range of motion evaluation




Range of motion, Physical therapy, Rehabilitation, Machine learning, Computer vision, Pose estimation


Joint range of motion (ROM) is an important quantitative measure for physical therapy. Commonly relying on a goniometer, accurate and reliable ROM measurement requires extensive training and practice. This, in turn, imposes a significant barrier for those who have limited in-person access to healthcare. The current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam. To evaluate its reliability, the ROM measurements for a diverse set of joints (neck, spine, and upper and lower extremities) derived using this method were compared to those obtained from a state-of-the-art marker-based, optical motion capture system. Results showed that the webcam-based solution provides high test-retest reliability and inter-rater reliability at a fraction of the cost of the marker-based system. More importantly, the machine-learning-based method has been shown to be more consistent in tracking joint positions during movements, making it more reliable than the optical motion capture system. The proposed webcam-based ROM evaluation method could be easily adapted for clinical practice and shows tremendous potential for the tele-implementation of physical therapy and rehabilitation.


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