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The use of deep neural networks in electromyogram (EMG) based prosthesescontrol provides a promising alternative to the hand-crafted features byautomatically learning muscle activation patterns from the EMG signals.Meanwhile, the use of raw EMG signals as input to convolution neural networks(CNN) offers a simple, fast, and ideal scheme for effective control ofprostheses. Therefore, this study investigates the relationship between windowlength and overlap, which may influence the generation of robust raw EMG2-dimensional (2D) signals for application in CNN. And a rule of thumb for aproper combination of these parameters that could guarantee optimal networkperformance was derived. Moreover, we investigate the relationship between theCNN receptive window size and the raw EMG signal size. Experimental resultsshow that the performance of the CNN increases with the increase in overlapwithin the generated signals, with the highest improvement of 9.49% accuracyand 23.33% F1-score realized when the overlap is 75% of the window length.Similarly, the network performance increases with the increase in receptivewindow (kernel) size. Findings from this study suggest that a combination of75% overlap in 2D EMG signals and wider network kernels may provide ideal motorintents classification for adequate EMG-CNN based prostheses control scheme.

Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification