Real-time Active Control of a Lower Limb Exoskeleton Based on sEMG

Abstract

With the development of robotics, lower limb exoskeleton robots have broad application prospects in helping patients with rehabilitation. However, the current operating modes of the commercial lower limb exoskeleton robots are mostly driving the lower limb dysfunction patients for rehabilitation walking training. In this case, the patients are only passively involved, lacking voluntary participation. In this paper, by collecting the sEMG signal of the lower extremity of the exoskeleton wearer and applying deep backpropagation (BP) neural network to predict the knee and hip joint angles in real time based on the root mean square (RMS) feature of the sEMG signal, the motion intention of the wearer can be detected to control the exoskeleton. The experimental results show that the method has satisfactory real-time performance and accuracy, and can effectively improve the voluntary participation of the wearer and enhance the synergy performance of the humanexoskeleton system.