Schweisfurth Meike A, Markovic Marko, Dosen Strahinja, Teich Florian, Graimann Bernhard, Farina Dario
Institute for NeuroRehabilitation Systems, University Medical Center Göttingen, Georg-August University, D-37075 Göttingen, Germany.
J Neural Eng. 2016 Oct;13(5):056010. doi: 10.1088/1741-2560/13/5/056010. Epub 2016 Aug 22.
A drawback of active prostheses is that they detach the subject from the produced forces, thereby preventing direct mechanical feedback. This can be compensated by providing somatosensory feedback to the user through mechanical or electrical stimulation, which in turn may improve the utility, sense of embodiment, and thereby increase the acceptance rate.
In this study, we compared a novel approach to closing the loop, namely EMG feedback (emgFB), to classic force feedback (forceFB), using electrotactile interface in a realistic task setup. Eleven intact-bodied subjects and one transradial amputee performed a routine grasping task while receiving emgFB or forceFB. The two feedback types were delivered through the same electrotactile interface, using a mixed spatial/frequency coding to transmit 8 discrete levels of the feedback variable. In emgFB, the stimulation transmitted the amplitude of the processed myoelectric signal generated by the subject (prosthesis input), and in forceFB the generated grasping force (prosthesis output). The task comprised 150 trials of routine grasping at six forces, randomly presented in blocks of five trials (same force). Interquartile range and changes in the absolute error (AE) distribution (magnitude and dispersion) with respect to the target level were used to assess precision and overall performance, respectively.
Relative to forceFB, emgFB significantly improved the precision of myoelectric commands (min/max of the significant levels) for 23%/36% as well as the precision of force control for 12%/32%, in intact-bodied subjects. Also, the magnitude and dispersion of the AE distribution were reduced. The results were similar in the amputee, showing considerable improvements.
Using emgFB, the subjects therefore decreased the uncertainty of the forward pathway. Since there is a correspondence between the EMG and force, where the former anticipates the latter, the emgFB allowed for predictive control, as the subjects used the feedback to adjust the desired force even before the prosthesis contacted the object. In conclusion, the online emgFB was superior to the classic forceFB in realistic conditions that included electrotactile stimulation, limited feedback resolution (8 levels), cognitive processing delay, and time constraints (fast grasping).
主动假肢的一个缺点是它使使用者与产生的力相分离，从而阻止了直接的机械反馈。这可以通过向用户提供体感反馈来补偿，通过机械或电刺激，这反过来可能会提高实用性、身体存在感，从而提高接受率。
在本研究中，我们在一个现实的任务设置中，使用电触觉接口，将一种新的闭环方法，即肌电图反馈（emgFB），与经典的力反馈（forceFB）进行了比较。11名身体健全的受试者和1名经桡骨截肢者在接受emgFB或forceFB的同时执行常规抓握任务。两种反馈类型通过相同的电触觉接口传递，使用空间/频率混合编码来传输反馈变量的8个离散级别。在emgFB中，刺激传递受试者产生的处理后的肌电信号的幅度（假肢输入），在forceFB中，传递产生的抓握力（假肢输出）。该任务包括在六种力下进行150次常规抓握试验，以五个试验（相同力）的块随机呈现。四分位距和相对于目标水平的绝对误差（AE）分布的变化（幅度和离散度）分别用于评估精度和整体性能。
相对于forceFB，emgFB显著提高了身体健全受试者肌电指令的精度（显著水平的最小值/最大值），提高了23%/36%，以及力控制的精度，提高了12%/32%。此外，AE分布的幅度和离散度降低。截肢者的结果相似，显示出相当大的改善。
因此，使用emgFB，受试者降低了前向通路的不确定性。由于肌电图和力之间存在对应关系，前者预测后者，emgFB允许进行预测控制，因为受试者在假肢接触物体之前就利用反馈来调整所需的力。总之，在包括电触觉刺激、有限的反馈分辨率（8个级别）、认知处理延迟和时间限制（快速抓握）的现实条件下，在线emgFB优于经典的forceFB。
