Abstract
To enable exoskeleton wearers to walk on level ground, estimation of lower limb movement is particularly indispensable. In fact, it allows the exoskeleton to follow the human movement in real time. In this paper, the general regression neural network optimized by golden section algorithm (GS-GRNN) is used to realize prediction of the human lower limb joint angle. The human body hip joint angle and the surface electromyographic (sEMG) signals of the thigh muscles are taken as the inputs of a neural network to predict joint angles of lower limbs. To improve the prediction accuracy in different gait phases, the plantar pressure signals are also added into the input. After that, the error between the prediction result and the actual data decreases significantly. Finally, compared with the prediction result of the BP neural network, GRNN shows splendid prediction performance for its less processing time and higher prediction accuracy.
Highlights
The issue of human–machine coordination has become one of the most important research problems in the field of robotics, especially for exoskeleton applications [1,2,3,4]
Chen Lingling proposed a natural regression algorithm based on support vector machine (SVM) to evaluate knee joint angle and realize EMG control of lower limb prosthesis [5]
Suncheol et al used back propagation neural network (BPNN) to predict elbow and shoulder joint angles based on upper limb EMG signals, integrated with angles calculated by dynamics, to avoid collision between the mechanical arm and the upper limbs [6]
Summary
The issue of human–machine coordination has become one of the most important research problems in the field of robotics, especially for exoskeleton applications [1,2,3,4]. Lower limb motion intention information can be got from the sEMG of the thigh muscle to realize motion pattern recognition [16], making it possible that the sEMG signal can be used as an input to the control system. This method can stimulate the patient’s active participation in consciousness and encourage patients to autonomously control the muscle contraction, which is more beneficial for the recovery of motor function [17]. In this paper, GS-GRNN is used to realize data prediction with the angle signals, sEMG signals and the plantar pressure signals integrated into the input data
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