The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network.

Xianfu Zhang,Ruimin Luo,Yuping Hu,Chao Li,Zhichuan Tang

doi:10.3390/s21248365

Abstract

Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait recognition, but several real-time limitations affect its applicability, of which variation in the load styles is obvious. The purposes of this study are to (1) investigate the impact of different load styles on gait recognition; (2) study whether good gait recognition performance can be obtained when a convolutional neural network (CNN) is used to deal with the sEMG image from sparse multichannel sEMG (SMC-sEMG); and (3) explore whether the control system of the lower-limb exoskeleton trained by sEMG from part of the load styles still works efficiently in a real-time environment where multiload styles are required. In addition, we discuss an effective method to improve gait recognition at the levels of the load styles. In our experiment, fifteen able-bodied male graduate students with load (20% of body weight) and using three load styles (SBP = backpack, SCS = cross shoulder, SSS = straight shoulder) were asked to walk uniformly on a treadmill. Each subject performed 50 continuous gait cycles under three speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h). A CNN was employed to deal with sEMG images from sEMG signals for gait recognition, and back propagation neural networks (BPNNs) and support vector machines (SVMs) were used for comparison by dealing with the same sEMG signal. The results indicated that (1) different load styles had remarkable impact on the gait recognition at three speeds under three load styles (p < 0.001); (2) the performance of gait recognition from the CNN was better than that from the SVM and BPNN at each speed (84.83%, 81.63%, and 83.76% at V3; 93.40%, 88.48%, and 92.36% at V5; and 90.1%, 86.32%, and 85.42% at V7, respectively); and (3) when all the data from three load styles were pooled as testing sets at each speed, more load styles were included in the training set, better performance was obtained, and the statistical analysis suggested that the kinds of load styles included in training set had a significant effect on gait recognition (p = 0.002), from which it can be concluded that the control system of a lower-limb exoskeleton trained by sEMG using only some load styles is not sufficient in a real-time environment.

Highlights

The support vector machines (SVMs) and back propagation neural networks (BPNNs) algorithms were employed to deal with the same data separately to verify our conclusion, and the results shown in Figure 3 showed that load styles had a significant effect on gait recognition, which was clearly obtained from SVM and BPNN (p < 0.05); the performances of SVM and BPNN were generally worse than that of the convolutional neural network (CNN) at the three tested speeds
We demonstrated that the different load styles had marked effects on gait recognition based on surface electromyogram (sEMG) images using a CNN, and the same conclusion was obtained from a BPNN and SVM, which means that the marked effect was not mainly caused by algorithms but load styles
The gait recognition based on SMC-sEMG images using the CNN was much better than those using the SVM and BPNN, indicating that the CNN has good performance in processing SMC-sEMG images for gait cognition in addition to high-density sEMG (HD-sEMG) images

Summary

Introduction

D et al [1] used the sEMG signal from the lower-limb muscles based on a Bayesian information criteria algorithm to recognize eight different gait phases for the control of exoskeletons. Chen Lingling et al [6] employed sEMG from leg muscles to recognize the gait phases and translated it as a switch signal of self-locking control to drive the lower-limb prostheses. Xu et al [7] used the sEMG signals from the calf muscle as the input of gait recognition (including go forward, go backward, turn left, and turn right), and the gait information was leveraged to control the motion of the lower-limb exoskeleton and realize human–computer interaction. Peng et al [2] proposed an SVM-based gait recognition method using sEMG signals to control a lower-limb prosthesis device

Methods

Results

Discussion

Conclusion