Matsumura Aoi, Ueda Atsushi, Nakamura Yasuo
Graduate School of Health and Sports Science, Doshisha University, Kyoto, Japan; TAKE PHYSICAL CONDITIONING Inc., Kyoto, Japan.
Graduate School of Health and Sports Science, Doshisha University, Kyoto, Japan.
J Electromyogr Kinesiol. 2019 Feb;44:46-55. doi: 10.1016/j.jelekin.2018.11.007. Epub 2018 Nov 13.
The conventional acromion marker cluster (AMC) method used to estimate scapular orientation cannot adequately represent complex shoulder movements due to soft tissue artifacts. The regression method may have nonlinear error changes depending on humeral elevation angle and elevation plane. Therefore, we aimed to develop a new method of estimating scapular orientation using curved surface interpolation during various shoulder movements, and to compare its accuracy with conventional and regression methods. Thirteen healthy men were recruited. AMC and refractive markers for bony landmarks were placed on the skin. During the preprocess, several shoulder postures, including different arm elevations and elevation planes, were measured using the motion capture system. Premeasured data were used to calibrate the positional relationship between AMC and scapula using curved surface interpolation. Subsequently, scapular orientations were estimated by measuring AMC and body markers of any shoulder posture. To evaluate the accuracy of our methods, 25 elevation postures and six tasks involving postures common to activities of daily living were applied. For tasks requiring greater arm elevation angles, the root mean square error was less in our method than in the conventional and regression methods. Therefore, our method could improve the accuracy of estimating scapular orientation in various elevation postures.
用于估计肩胛方位的传统肩峰标记簇(AMC)方法,由于软组织伪影,无法充分体现复杂的肩部运动。回归方法可能会因肱骨抬高角度和抬高平面而产生非线性误差变化。因此,我们旨在开发一种在各种肩部运动期间使用曲面插值估计肩胛方位的新方法,并将其准确性与传统方法和回归方法进行比较。招募了13名健康男性。在皮肤上放置了用于骨性标志的AMC和反光标记。在预处理过程中,使用动作捕捉系统测量了几种肩部姿势,包括不同的手臂抬高和抬高平面。预先测量的数据用于通过曲面插值校准AMC与肩胛骨之间的位置关系。随后,通过测量任何肩部姿势的AMC和身体标记来估计肩胛方位。为了评估我们方法的准确性,应用了25种抬高姿势和六项涉及日常生活活动常见姿势的任务。对于需要更大手臂抬高角度的任务,我们的方法的均方根误差比传统方法和回归方法更小。因此,我们的方法可以提高在各种抬高姿势下估计肩胛方位的准确性。