van den Hoorn Wolbert, Lavaill Maxence, Hollman Freek, Valero Roberto Pareyón, Bruyer-Montéléone François, Cutbush Kenneth, Gupta Ashish, Kerr Graham
School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
Queensland Unit for Advanced Shoulder Research, Brisbane, QLD, Australia.
Shoulder Elbow. 2025 Aug 7:17585732251360746. doi: 10.1177/17585732251360746.
Smartphone-based 2D-pose estimation offers a convenient method for assessing shoulder range-of-motion (ROM), but its accuracy compared to 3D motion capture (3D-mocap) needs to be determined.
Shoulder ROM was recorded in seventeen participants without shoulder issues using 3D-mocap and 2D-pose concurrently. Movements included abduction, flexion, extension, external, and functional internal rotation (IR). 2D-pose ROM estimates (mymobility's Skeletal Tracking Shoulder Range of Motion Assessments feature (Apple Vision framework, Apple Inc., Cupertino, CA, USA) were compared to 3D-mocap using linear mixed-models and Bland-Altman analysis. The influence of thoracic compensation and anatomical frame definitions on shoulder ROM estimates was examined.
High consistency was observed between 2D-pose and 3D-mocap ( > 0.98), especially for abduction and flexion. Differences in ROM were linked to anatomical frame variations and thoracic contributions, with 2D-pose overestimating ROM at greater ranges (2°-25°). Internal rotation zone identification showed high consistency, but 2D-pose-based extension and external rotation showed more variability due to thoracic compensation.
Smartphone-based 2D-pose estimation provides a valid alternative for shoulder ROM measurement but should not be used interchangeably with 3D-mocap due to discrepancies arising from anatomical frame definitions and thoracic movements. Shoulder ROM assessment requires consideration of these limitations to ensure appropriate clinical interpretation.
基于智能手机的二维姿势估计为评估肩部活动范围（ROM）提供了一种便捷的方法，但其与三维运动捕捉（3D-mocap）相比的准确性有待确定。
使用3D-mocap和二维姿势同时记录了17名无肩部问题参与者的肩部ROM。运动包括外展、屈曲、伸展、外旋和功能性内旋（IR）。使用线性混合模型和布兰德-奥特曼分析将二维姿势ROM估计值（mymobility的骨骼跟踪肩部活动范围评估功能（苹果视觉框架，苹果公司，美国加利福尼亚州库比蒂诺））与3D-mocap进行比较。研究了胸廓代偿和解剖框架定义对肩部ROM估计值的影响。
二维姿势和3D-mocap之间观察到高度一致性（>0.98），尤其是在外展和屈曲方面。ROM的差异与解剖框架变化和胸廓贡献有关，二维姿势在更大范围（2°-25°）高估了ROM。内旋区域识别显示出高度一致性，但由于胸廓代偿，基于二维姿势的伸展和外旋显示出更多变异性。
基于智能手机的二维姿势估计为肩部ROM测量提供了一种有效的替代方法，但由于解剖框架定义和胸廓运动产生的差异，不应与3D-mocap互换使用。肩部ROM评估需要考虑这些局限性，以确保进行适当的临床解读。
