Gait recognition based on sEMG and Deep Residual Shrinkage Network

Transverse resistance exoskeleton is expected to provide better rehabilitation training methods for patients with lower limb injury. Lateral walking gait recognition plays an important role in lateral resistance exoskeleton control. In the phase of lateral walking out of synchrony, the EMG signals produced by the thighs will be different. The EMG signals collected by five subjects while walking laterally were used in the experiment. The four muscles are: the gluteus medius of the left leg, the tensor fascia lata, the adductor major, and the tensor fascia lata of the right leg. Based on the one-dimensional EMG signals of the four muscles, this paper uses the classification model commonly used in the MATLAB toolbox for training and recognition. However, the experimental results show that TREE and KNN have poor recognition accuracy for the four gait stages. To this end, this paper applies the convolutional residual shrinkage network (DRSN) to this occasion. The collected one-dimensional EMG signals are plotted as pictures and fed into the model for training and classification. Experimental results show that the classification accuracy is greatly improved compared with the previous model.