A myoelectric prosthetic hand with muscle synergy-based motion determination and impedance model-based biomimetic control
A myoelectric prosthetic hand with muscle synergy-based motion determination and impedance model-based biomimetic control
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DOI：
10.1126/scirobotics.aaw6339
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发表时间：
2019-06-26
期刊：
影响因子：
25
通讯作者：
Tsuji, Toshio
中科院分区：
文献类型：
--
作者：
Furui, Akira;Eto, Shintaro;Tsuji, Toshio
假手是为因事故或疾病而上肢截肢的病人开的处方。这样做是为了让患者恢复失去的手的功能。肌电假手被发现有可能实现基于操作者的肌电图（EMG）信号的直观控制。对这些控制措施进行了广泛的研究和开发。近年来，通过三维（3D）打印技术，假手的开发成本和可维护性得到了改善。然而，没有以前的研究已经意识到的优势，结合引入先进的控制机制，基于人体运动的多个手指运动的EMG为基础的分类。提出了一种3D打印肌电假手及其控制系统。该系统引入了基于肌肉协同的运动确定方法和仿生阻抗控制，实现了假手手指运动的分类和平滑直观。我们评估所提出的系统，通过操作实验进行六个健康的参与者和上肢截肢者参与者。实验结果表明，我们的假手系统可以成功地分类学习单一的动作和未学习的组合动作从肌电信号具有高度的准确性。此外，假肢手的实际使用的应用程序证明通过截肢者参与者进行的控制任务。
Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator's electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy-based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.