Department of Biomedical Engineering, Case Western Reserve University, 10,900 Euclid Avenue, Cleveland, OH, 44106-1712, USA.
Cleveland FES Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Boulevard, B-E210, Cleveland, OH, 44106-1702, USA.
J Neuroeng Rehabil. 2019 Nov 21;16(1):147. doi: 10.1186/s12984-019-0607-8.
Modern prosthetic hands are typically controlled using skin surface electromyographic signals (EMG) from remaining muscles in the residual limb. However, surface electrode performance is limited by changes in skin impedance over time, day-to-day variations in electrode placement, and relative motion between the electrodes and underlying muscles during movement: these limitations require frequent retraining of controllers. In the presented study, we used chronically implanted intramuscular electrodes to minimize these effects and thus create a more robust prosthetic controller.
A study participant with a transradial amputation was chronically implanted with 8 intramuscular EMG electrodes. A K Nearest Neighbor (KNN) regression velocity controller was trained to predict intended joint movement direction using EMG data collected during a single training session. The resulting KNN was evaluated over 12 weeks and in multiple arm posture configurations, with the participant controlling a 3 Degree-of-Freedom (DOF) virtual reality (VR) hand to match target VR hand postures. The performance of this EMG-based controller was compared to a position-based controller that used movement measured from the participant's opposite (intact) hand. Surface EMG was also collected for signal quality comparisons.
Signals from the implanted intramuscular electrodes exhibited less crosstalk between the various channels and had a higher Signal-to-Noise Ratio than surface electrode signals. The performance of the intramuscular EMG-based KNN controller in the VR control task showed no degradation over time, and was stable over the 6 different arm postures. Both the EMG-based KNN controller and the intact hand-based controller had 100% hand posture matching success rates, but the intact hand-based controller was slightly superior in regards to speed (trial time used) and directness of the VR hand control (path efficiency).
Chronically implanted intramuscular electrodes provide negligible crosstalk, high SNR, and substantial VR control performance, including the ability to use a fixed controller over 12 weeks and under different arm positions. This approach can thus be a highly effective platform for advanced, multi-DOF prosthetic control.
现代假肢手通常使用残肢中剩余肌肉的表面肌电信号（EMG）进行控制。然而，表面电极的性能受到皮肤阻抗随时间的变化、电极放置位置的日常变化以及运动过程中电极和下面肌肉之间的相对运动的限制：这些限制需要频繁地重新训练控制器。在本研究中，我们使用慢性植入式肌内电极来最小化这些影响，从而创建更强大的假肢控制器。
一名桡骨截断截肢患者被慢性植入 8 个肌内 EMG 电极。使用 K 最近邻（KNN）回归速度控制器来训练预测关节运动方向，使用单次训练期间收集的 EMG 数据。使用参与者控制 3 自由度（DOF）虚拟现实（VR）手来匹配目标 VR 手姿势的方法，对生成的 KNN 进行了 12 周和多个手臂姿势配置的评估。将基于肌电的控制器的性能与使用来自参与者对侧（完好）手的运动测量的位置基控制器进行了比较。还收集了表面 EMG 用于信号质量比较。
植入式肌内电极的信号表现出各通道之间的串扰较小，信噪比高于表面电极信号。基于肌电的 KNN 控制器在 VR 控制任务中的性能随时间没有下降，并且在 6 种不同的手臂姿势下都很稳定。基于肌电的 KNN 控制器和基于完好手的控制器在手姿势匹配成功率均为 100%，但基于完好手的控制器在速度（试验时间）和 VR 手控制的直接性（路径效率）方面略有优势。
慢性植入式肌内电极提供可忽略的串扰、高 SNR 和可观的 VR 控制性能，包括在 12 周和不同手臂位置下使用固定控制器的能力。因此，这种方法可以成为高级、多自由度假肢控制的高度有效的平台。
