Abstract
When patients wear an exoskeleton for rehabilitation to walk and encounter obstacles or stairs, they often need manual operation by themselves or other people to switch the gait of the exoskeleton robot, which leads to a poor human-machine synergy of common exoskeleton robots. In this paper, the surface electromyography (sEMG) signals of rectus femoris (RF), semimembranosus (SM) and sartorius (SR) muscles of the subjects were collected and the time domain features - root mean square (RMS) of the sEMG were extracted. A back propagation (BP) neural network was used to predict knee joint angles in real time, when the exoskeleton wearer’s knee angle was greater than 70°, the exoskeleton performed gait switching across the obstacle in real time. Experiments show that when encountering obstacles, the method can realize the real-time gait switching of the exoskeleton across obstacles by the wearer using sEMG signals, and the method has good real-time characteristic and accuracy.
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