Electromyography (EMG) has been widely accepted to interact with prosthetic hands, but still limited to using few channels for the control of few degrees of freedom. The use of more channels can improve the controllability, but it also increases system’s complexity and reduces its wearability. It is yet clear if optimizely placing the EMG channel could provide a feasible solution to this challenge. This study customized a genetic algorithm to optimize the number of channels and its position on the forearm in inter-day hand gesture recognition scenario. Our experimental results demonstrate that optimally selected 14 channels out of 16 can reach a peak inter-day hand gesture recognition accuracy at 72.3%, and optimally selecting 9 and 11 channels would reduce the performance by 3% and 10%. The cross-validation results also demonstrate that the optimally selected EMG channels from five subjects also work on the rest of the subjects, improving the accuracies by 3.09% and 4.5% in 9- and 11-channel combination, respectively. In sum, this study demonstrates the feasibility of channel reduction through genetic algorithm, and preliminary proves the significance of EMG channel optimization for human–machine interaction.

中文翻译：

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通过渠道优化促进基于 sEMG 的人机交互

肌电图 (EMG) 已被广泛接受与假手交互，但仍仅限于使用少数通道来控制少数自由度。使用更多的通道可以提高可控性，但也增加了系统的复杂性，降低了其可穿戴性。目前尚不清楚优化放置 EMG 通道是否可以为这一挑战提供可行的解决方案。本研究定制了一种遗传算法，以优化日间手势识别场景中通道数量及其在前臂上的位置。我们的实验结果表明，在 16 个通道中最优选择 14 个通道可以达到 72.3% 的峰值日间手势识别准确率，而最优选择 9 和 11 个通道会使性能降低 3% 和 10%。交叉验证结果还表明，来自 5 个受试者的最佳 EMG 通道也适用于其余受试者，在 9 通道和 11 通道组合中分别提高了 3.09% 和 4.5% 的准确度。综上所述，本研究论证了通过遗传算法进行通道缩减的可行性，初步证明了肌电通道优化对人机交互的意义。