LAUSR

This article presents a graph convolutional network model (GCNM) for gait phase classification to control a lower limb exoskeleton. The GCNM can recognize four leg phases between the foot and ground, including heel strike, foot flat, heel off, and swing. The proposed model can solve the gait phase classification problem from non-Euclidean domain based on a graph mechanism for exoskeletons. Real-time performance of the gait data acquisition system was then verified on a lower limb exoskeleton. The system quantifies the time delay with an optoelectronic system, VICON BONITA 10. Comparison works with some existing state-of-the-art methods from the Euclidean domain, i.e., long short-term memory (LSTM) and deep convolutional neural network (DCNN), were also provided. Experimental works show that the proposed model has a significantly higher prediction accuracy and better robustness in gait phase classification for different people in level, uphill, and downhill environments. Moreover, the label rate is under ${\rm {10}}{\rm {\% }}$, compared to LSTM and DCNN, which are usually at ${\rm {70}}$–${\rm {80}}{\rm {\% }}$. Finally, the GCNM's maximum accuracy of gait phase classification is ${\rm {97}}{\rm {.43\% }}$; thus, the effectiveness of the proposed model is verified.