Journal
INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS
Volume 16, Issue 4, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219843619410019
Keywords
Hand motion; electromyography; pattern recognition; genetic algorithm
Categories
Funding
- 7th framework programme of the European Union [600915]
- open fund of the key laboratory for metallurgical equipment and control of ministry of education in wuhan university of science and technology [2017B03]
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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.
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