Human muscle synergy– and impedance model–based control enables accurate and natural finger motions of a 3D-printed myoelectric hand. 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.

中文翻译：

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肌电假肢，具有基于肌肉协同作用的运动确定和基于阻抗模型的仿生控制

基于人体肌肉协同和阻抗模型的控制可实现3D打印的肌电手的准确自然的手指运动。对于因意外事故或疾病而截肢的患者，必须使用假肢手。这样做是为了让患者恢复失去的手的功能。发现肌电假手可以根据操作员的肌电图（EMG）信号实施直观的控制。这些控件已被广泛研究和开发。近年来，通过三维（3D）打印技术已经改善了假手的开发成本和可维护性。然而，以前的研究还没有意识到基于EMG的多指运动分类的优点，以及引入基于人体运动的高级控制机制的优势。本文提出了一种3D打印的肌电修复手和一个随附的控制系统。在提议的系统中引入了基于肌肉协同作用的运动确定方法和仿生阻抗控制，从而可以对假肢的未学习的组合运动和平滑而直观的手指运动进行分类。我们通过对六个健康参与者和一个上肢截肢者参与者进行的操作实验来评估所提议的系统。实验结果表明，我们的假肢手系统可以高度准确地对来自EMG信号的学习到的单个动作和未学习到的组合动作进行成功分类。此外，通过截肢者参与者执行的控制任务，展示了假肢手在现实世界中的应用。