Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis

Abstract

Objective. In order to study the motion recognition intention of lower limb prosthesis based on the CNN deep learning algorithm. Methods. A convolutional neural network (CNN) model was established to reconstruct the motion pattern. Before the movement mode of the affected side was converted, the sensor was bound to the healthy side. The classifier was employed to extract and classify the features, so as to realize the accurate description of the movement intention of the disabled. Results. The method proposed in this research can achieve 98.2% recognition rate of the movement intention of patients with lower limb amputation under different terrains, and the recognition rate can reach 97% after the pattern converted between the five modes was added. Conclusion. The deep learning algorithm that automatically recognized and extracted features can effectively improve the control performance on the intelligent lower limb prosthesis and realize the natural and seamless conversion of the intelligent prosthesis in a variety of motion modes.