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