The pattern-recognition-based control of myoelectric prostheses offers amputees a natural, intuitive approach to more finely control the prostheses. In this context, we recently developed a multichannel surface electromyography (sEMG) module with a low sampling rate and applied it for hand-motion research. In this study, we investigate the effects of the sEMG-signal sampling rate and feature extraction window length on the classification accuracy in hand-motion recognition. Ten normal subjects and one forearm amputee were made to wear an armband module consisting of eight EMG sensors, and seven and four hand movements of the normal subjects and amputee, respectively, were measured. The EMG signal was measured at 500 Hz and down-sampled to 250, 100, and 50 Hz. Four time-domain features (mean average value, waveform length, zero crossing, and slope sign change) were calculated as the sEMG features with six selected window lengths, which were increased in 50 ms intervals (50, 100, 150, 200, 250, and 300 ms). Hand-motion recognition was performed using artificial neural network, support vector machine, decision tree, and k-nearest neighbor classifiers. Our results showed that for all classifiers and all subjects, the hand-motion classification accuracy increases with an increase in the sampling rate and window length. We believe that our findings will aid in selecting the appropriate sampling rate and window length for prosthetics meant for daily use.
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This research was supported by the Bio & Medical Technology Development Program (2017M3A9E2063270) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
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Kim, T., Kim, J., Koo, B. et al. Effects of Sampling Rate and Window Length on Motion Recognition Using sEMG Armband Module. Int. J. Precis. Eng. Manuf. 22, 1401–1411 (2021). https://doi.org/10.1007/s12541-021-00546-6
- Armband module
- Sampling rate
- Window length