Hamdan Sara, Oztop Erhan, Furukawa Jun-Ichiro, Morimoto Jun, Ugurlu Barkan
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1481-1484. doi: 10.1109/EMBC.2018.8512564.
In this paper, the shoulder glenohumeral displacement during the movement of the upper arm is studied. Four modeling approaches were examined and compared to estimate the humeral head elevation (vertical displacement) and translation (horizontal displacement). A biomechanics-inspired method was used firstly to model the glenohumeral displacement in which a least squares method was implemented for parameter identification. Then, three Gaussian process regression models were used in which the following variable sets were employed: i) shoulder adduction/abduction angle, ii) combination of shoulder adduction/abduction and flexion/extension angles, iii) overall upper arm orientation in the form of quaternions. In order to test the respective performances of these four models, we collected motion capture data and compared the models' representative capabilities. As a result, Gaussian process regression that considered the overall upper arm orientation outperformed the other modeling approaches; however, it should be noted that the other methods also provided accuracy levels that may be sufficient depending on task requirements.
本文研究了上臂运动过程中肩关节盂肱关节的位移。研究并比较了四种建模方法，以估计肱骨头的抬高（垂直位移）和平移（水平位移）。首先使用一种受生物力学启发的方法对盂肱关节位移进行建模，其中采用最小二乘法进行参数识别。然后，使用了三个高斯过程回归模型，其中采用了以下变量集：i）肩关节内收/外展角度，ii）肩关节内收/外展和屈伸角度的组合，iii）以四元数形式表示的上臂整体方向。为了测试这四种模型各自的性能，我们收集了运动捕捉数据并比较了模型的代表性能力。结果，考虑上臂整体方向的高斯过程回归优于其他建模方法；然而，应该注意的是，根据任务要求，其他方法也提供了足够的精度水平。
