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Hand and Wrist Movement Control of Myoelectric Prosthesis Based on Synergy

机译:基于协同作用的肌电假体手腕运动控制

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This study proposes a method to control a prosthetic hand by EMG signals based on muscle synergies. The muscle synergy model suggests a framework to transform commands of the central nervous system to a set of complex muscular movements. Using this method, we have tried to realize the proportional control of multiple degrees of freedom (DOF). This study focuses on controlling four kinds of hand/wrist movements of the prosthesis: open, close, pronate, and supinate. The nonnegative matrix factorization (NMF) algorithm is used to map muscle activities into these four movements through the calculation of a muscle synergy matrix. An EMG feature selection process along with a control scheme has been added, which smooths the output thereby stabilizing the movements. Ten healthy subjects performed an online experiment comprised of two tests: 1) proportional control on single DOF, and 2) simultaneous control of multiple DOFs. The results indicate that fluid hand/wrist movements could be estimated from EMG. The average values achieved by all subjects for the single-DOF test and the multiple-DOF test are 0.97 and 0.93, respectively.
机译:这项研究提出了一种基于肌肉协同作用的肌电信号来控制假手的方法。肌肉协同模型建议将中枢神经系统的命令转换为一组复杂的肌肉运动的框架。使用这种方法,我们试图实现多自由度(DOF)的比例控制。这项研究的重点是控制假体的四种手部/腕部运动:张开,闭合,前屈和仰卧。非负矩阵分解(NMF)算法用于通过计算肌肉协同矩阵将肌肉活动映射到这四个运动中。已添加了EMG特征选择过程以及控制方案,该过程使输出平滑,从而稳定了运动。十名健康受试者进行了包含两个测试的在线实验:1)对单个自由度进行比例控制,以及2)同时控制多个自由度。结果表明,可以根据肌电图估计手/腕的流畅运动。所有受试者在一次自由度测试和多次自由度测试中获得的平均值分别为0.97和0.93。

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