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Background. Joint range of motion (ROM) is an important quantitative measurefor physical therapy. Commonly relying on a goniometer, accurate and reliableROM measurement requires extensive training and practice. This, in turn,imposes a significant barrier for those who have limited in-person access tohealthcare.  Objective. The current study presents and evaluates an alternative machinelearning-based ROM evaluation method that could be remotely accessed via awebcam.  Methods. To evaluate its reliability, the ROM measurements for a diverse setof joints (neck, spine, and upper and lower extremities) derived using thismethod were compared to those obtained from a marker-based optical motioncapture system.  Results. Data collected from 25 healthy adults demonstrated that the webcamsolution exhibited high test-retest reliability, with substantial to almostperfect intraclass correlation coefficients for most joints. Compared with themarker-based system, the webcam-based system demonstrated substantial to almostperfect inter-rater reliability for some joints, and lower inter-raterreliability for other joints (e.g., shoulder flexion and elbow flexion), whichcould be attributed to the reduced sensitivity to joint locations at the apexof the movement.  Conclusions. The proposed webcam-based method exhibited high test-retest andinter-rater reliability, making it a versatile alternative for existing ROMevaluation methods in clinical practice and the tele-implementation of physicaltherapy and rehabilitation.

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