PDF(Pubmed)
Abstract：
BACKGROUND: The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined.
METHODS: Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple\'s vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis.
RESULTS: We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements.
CONCLUSIONS: Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions.
METHODS: Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple\'s vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis.
RESULTS: We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements.
CONCLUSIONS: Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions.
摘要：
背景：使用智能手机相机（2D-pose）量化肩部运动范围（RoM）的人体姿态跟踪的准确性尚未确定。
方法：招募20名健康个体并进行肩展，内收,屈曲，或扩展名，同时捕获使用基于智能手机的人体姿态估计算法（苹果的视觉框架），并使用基于皮肤标记的3D运动捕捉系统。通过将2D姿态结果与完善的3D运动捕获协议进行比较来评估有效性。此外,使用三部智能手机在多个垂直和水平位置调查了iPhone定位的影响。使用线性混合模型和Bland-Altman分析对其关系和有效性进行了分析。
结果：我们发现基于2D姿势的肩部RoM与3D运动捕捉（线性混合模型：R2>0.93）一致，但在智能手机上有些高估了。差异取决于肩部运动类型和RoM振幅，在所有测试的动作中，内收表现最差。使用线性方程描述所有运动类型。提供校正方法以校正潜在的平面外肩部运动。
结论：使用智能手机摄像头估算的肩部RoM与3D运动捕获衍生的RoM一致；但是，观察到系统之间的差异,并可能通过胸框定义的差异来解释.
方法：招募20名健康个体并进行肩展，内收,屈曲，或扩展名，同时捕获使用基于智能手机的人体姿态估计算法（苹果的视觉框架），并使用基于皮肤标记的3D运动捕捉系统。通过将2D姿态结果与完善的3D运动捕获协议进行比较来评估有效性。此外,使用三部智能手机在多个垂直和水平位置调查了iPhone定位的影响。使用线性混合模型和Bland-Altman分析对其关系和有效性进行了分析。
结果：我们发现基于2D姿势的肩部RoM与3D运动捕捉（线性混合模型：R2>0.93）一致，但在智能手机上有些高估了。差异取决于肩部运动类型和RoM振幅，在所有测试的动作中，内收表现最差。使用线性方程描述所有运动类型。提供校正方法以校正潜在的平面外肩部运动。
结论：使用智能手机摄像头估算的肩部RoM与3D运动捕获衍生的RoM一致；但是，观察到系统之间的差异,并可能通过胸框定义的差异来解释.
