Generative sEMG Deep Learning for Early Prediction of Locomotion with Small Training Datasets

Early detection of locomotion intention is highly relevant to the development of intelligent rehabilitation/assistive robotics. While surface electromyography(sEMG) has been a promising tool, it is often challenged by the shear variability of sEMG patterns in contrast to only a handful of sEMG training samples per discrete motion intention class for each individual user to begin with. To address this issue, we introduce a deep convolutional generative adversarial networks (DCGANs), including dynamic time warping (DTW) and fast Fourier transform mean square error (FFT MSE) for artificial signal quality assessment. On a preliminary sEMG data set of 3-class directional lower-limb movement, the proposed method yielded an average accuracy rate of $89.31 \% \pm 6.52$ . While this is a feasibility study using healthy human subjects only, the result warrants extended study to further establish the generative adversarial network learning for EMG intention detection in real-world rehabilitation/assistive system applications.