Institute of Robotics and Mechatronics, DLR-German Aerospace Center, Wessling, Germany. Author to whom any correspondence should be addressed.
J Neural Eng. 2020 Mar 27;17(2):026011. doi: 10.1088/1741-2552/ab7b1e.
Myocontrol, that is, control of a prosthesis via muscle signals, is still a surprisingly hard problem. Recent research indicates that surface electromyography (sEMG), the traditional technique used to detect a subject's intent, could proficiently be replaced, or conjoined with, other techniques (multi-modal myocontrol), with the aim to improve both on dexterity and reliability. Objective. In this paper we present an online assessment of multi-modal sEMG and force myography (FMG) targeted at hand and wrist myocontrol. Approach. Twenty sEMG and FMG sensors in total were used to enforce simultaneous and proportional control of hand opening/closing, wrist pronation/supination and wrist flexion/extension of 12 intact subjects. Main results and Significance. We found that FMG yields in general a better performance than sEMG, and that the main drawback of the sEMG array we used is not the inability to perform a desired action, but rather action interference, that is, the undesired concurrent activation of another action. FMG, on the other hand, causes less interference.
肌电控制，即通过肌肉信号控制假肢，仍然是一个极具挑战性的问题。最近的研究表明，表面肌电图（sEMG）作为传统的用于检测主体意图的技术，可以被其他技术（多模态肌电控制）高效地替代或结合，以提高灵巧性和可靠性。目的。本文提出了一种针对手部和腕部肌电控制的多模态 sEMG 和力肌电图（FMG）的在线评估。方法。共使用 20 个 sEMG 和 FMG 传感器，以强制 12 名完整受试者同时进行手部开合、手腕旋前/旋后和手腕屈伸的比例控制。主要结果和意义。我们发现 FMG 的性能普遍优于 sEMG，而且我们使用的 sEMG 阵列的主要缺点不是无法执行所需的动作，而是动作干扰，即不希望的同时激活另一个动作。另一方面，FMG 引起的干扰较小。
