A pattern recognition method of lower limb movements based on convolutional neural network

Abstract

In order to solve the problems of low data and low recognition rate in the current pattern recognition technology of lower limb movements,a new lower limb movement pattern recognition method based on convolutional neural network was proposed.The lower limb gait movements recognition was taken as the object,and the surface electromyography (sEMG) signals of five gaits of walking on flat ground without weight,going up/down stairs without weight,and going up/down stairs with weight were collected.Based on the feature extraction of sEMG,a convolutional neural network with feature set as input was constructed,and the recognition accuracy and working characteristics of several other classification and recognition methods were compared.The experimental results show that the average recognition accuracy of this method for five gaits is greater than 95%,and the error rate is less than 8%,which has high accuracy.The input feature set of the method can better represent the characteristics of the prediction model,and the pattern recognition rate is higher,which provides some reference for the improvement of lower limb motor function of rehabilitation medical robots,power-assisted robots and other equipment.

Keywords

• pattern recognition; surface electromyography signal (semg); convolutional neural network; feature extraction; classification and recognition; lower limb movements