Tanaka Taichi, Nambu Isao, Wada Yasuhiro
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4882-4885. doi: 10.1109/EMBC44109.2020.9175941.
Many researchers have developed assist-suits to support repetitive and strenuous physical labor, but existing suits show unsatisfactory responsiveness and restrict arm motions. Therefore, we propose a method for an arm-assist-suit that synchronizes arm motions by using electromyography (EMG) to predict arm trajectory. EMG is used to measure and record electrical signals while muscles are active. Further, predicted arm-joint motions and estimated arm-joint angles are used for arm trajectory predictions. In this study, we attempted the prediction of elbow-joint motions and the timing of motion changes. Two subjects executed twelve types of elbow-joint movements that had four start and endpoints. We measured seven muscle types with EMG points on the right arm(hand, elbow, and shoulder) a motion capture system, respectively. After processing these data, we applied a multiclass logistic regression, which is a machine-learning technique, to predict elbow-joint motions, namely, rest, flexion, and extension. The precision in elbow joint motion prediction shows a difference between the two subjects for the three motions analyzed. Additionally, the rest prediction accuracy is lower than both flexion and extension for each subject. The prediction of elbow-joint motion change timing does not correlate with the elbow-joint motion predictions, with the timing prediction precision being very low and thus, causing some difficulties. To overcome these difficulties, and improve precision in future work, we plan to apply an independent component analysis to eliminate noise and add or change features.Clinical Relevance- This study aims to establish a benchmark for future research on the improvement of responsiveness and range-of-motion of arm-assist-suits.
许多研究人员已开发出辅助套装来支持重复性的繁重体力劳动，但现有的套装表现出不尽人意的响应能力，并会限制手臂动作。因此，我们提出了一种用于手臂辅助套装的方法，该方法通过使用肌电图（EMG）来预测手臂轨迹，从而使手臂动作同步。EMG用于在肌肉活动时测量和记录电信号。此外，预测的手臂关节运动和估计的手臂关节角度用于手臂轨迹预测。在本研究中，我们尝试预测肘关节运动以及运动变化的时机。两名受试者执行了十二种类型的肘关节运动，这些运动有四个起始点和终点。我们分别使用位于右臂（手部、肘部和肩部）的EMG点以及运动捕捉系统测量了七种肌肉类型。处理这些数据后，我们应用了一种机器学习技术——多类逻辑回归来预测肘关节运动，即静止、弯曲和伸展。在所分析的三种运动中，肘关节运动预测的精度在两名受试者之间存在差异。此外，对于每个受试者，静止状态的预测准确率低于弯曲和伸展状态。肘关节运动变化时机的预测与肘关节运动预测不相关，时机预测精度非常低，因此造成了一些困难。为了克服这些困难并在未来的工作中提高精度，我们计划应用独立成分分析来消除噪声并添加或改变特征。临床意义——本研究旨在为未来关于提高手臂辅助套装响应能力和运动范围的研究建立一个基准。
