Background. 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. Objective. The current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam. Methods. 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 marker-based optical motion capture system. Results. Data collected from 25 healthy adults demonstrated that the webcam solution exhibited high test-retest reliability, with substantial to almost perfect intraclass correlation coefficients for most joints. Compared with the marker-based system, the webcam-based system demonstrated substantial to almost perfect inter-rater reliability for some joints, and lower inter-rater reliability for other joints (e.g., shoulder flexion and elbow flexion), which could be attributed to the reduced sensitivity to joint locations at the apex of the movement. Conclusions. The proposed webcam-based method exhibited high test-retest and inter-rater reliability, making it a versatile alternative for existing ROM evaluation methods in clinical practice and the tele-implementation of physical therapy and rehabilitation.

翻译：背景：关节活动度（ROM）是物理治疗中一项重要的量化指标。传统上依赖量角器进行准确可靠的ROM测量需要大量的培训和实践，这反过来给无法直接接触医疗服务的群体带来了显著障碍。目的：本研究提出并评估了一种基于机器学习的替代性ROM评估方法，该方法可通过网络摄像头远程访问。方法：为评估其可靠性，将该方法测量的多类关节（颈部、脊柱、上肢和下肢）ROM数据与基于标记点的光学运动捕捉系统所得数据进行对比。结果：来自25名健康成年人的数据表明，网络摄像头方案表现出较高的重测信度，多数关节的组内相关系数达到显著至近乎完美的水平。与基于标记点的系统相比，基于网络摄像头的系统在部分关节上显示出显著至近乎完美的评分者间信度，而在其他关节（如肩关节屈曲和肘关节屈曲）上信度较低，这可能归因于对运动顶点关节位置敏感性的降低。结论：所提出的基于网络摄像头的方法展现出较高的重测信度和评分者间信度，使其成为临床实践中现有ROM评估方法及物理治疗与康复远程实施中一种多功能替代方案。