Research on Pattern Recognition of Lower Limb Motion Based on Convolutional Neural Network

Abstract

Accurate motion recognition is essential for assist devices such as exoskeletons to achieve human-robot communion. However, at present, the technology of lower limb motion pattern recognition still has the problems of small amount of data and low recognition accuracy. In this paper, the lower limb motion was taken as the object, and the surface electromyography (sEMG) signals of five gaits of going upstairs without weight, going downstairs without weight, going upstairs with weight, going downstairs with weight, and walking on a level surface without weight were collected. Based on the feature extraction of the sEMG signal, a convolutional neural network (CNN) with a feature set as the input is constructed, and a new lower limb motion pattern recognition method is proposed. The recognition accuracy and work feature of the proposed method are compared with several other classification and recognition methods. The experimental results show that, compared with the traditional methods, the method of using the feature set as input for CNN can better represent the features of the prediction model, and the pattern recognition accuracy is higher. The recognition accuracy of the five gaits are all greater than 96.96%, and the error rate is less than 7%, indicating that the proposed method has higher recognition accuracy. This method provides theoretical support for achieving compliant power assistance and promoting motor function rehabilitation with rehabilitation robots, power-assisted robots, and other equipment.