Furui Akira, Eto Shintaro, Nakagaki Kosuke, Shimada Kyohei, Nakamura Go, Masuda Akito, Chin Takaaki, Tsuji Toshio
Graduate School of Engineering, Hiroshima University, Hiroshima, Japan.
Robot Rehabilitation Center in The Hyogo Institute of Assistive Technology, Kobe, Japan.
Sci Robot. 2019 Jun 26;4(31). doi: 10.1126/scirobotics.aaw6339.
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.
假肢手被开给因事故或疾病而上肢截肢的患者。这样做是为了让患者恢复失去手部的功能。人们发现肌电假肢手有可能基于操作者的肌电图（EMG）信号实现直观控制。这些控制方法已经得到了广泛的研究和发展。近年来，通过三维（3D）打印技术，假肢手的开发成本和可维护性得到了改善。然而，以前没有研究结合基于人体运动的先进控制机制来实现基于肌电的多手指运动分类的优势。本文提出了一种3D打印的肌电假肢手及其配套的控制系统。在所提出的系统中引入了基于肌肉协同的运动确定方法和仿生阻抗控制，能够对未学习的组合运动进行分类，并使假肢手实现平滑且直观的手指运动。我们通过对六名健康参与者和一名上肢截肢参与者进行的操作实验来评估所提出的系统。实验结果表明，我们的假肢手系统能够从肌电信号中成功地以高精度对已学习的单个运动和未学习的组合运动进行分类。此外，通过截肢参与者进行的控制任务展示了假肢手在实际应用中的情况。
