Estimation of Continuous Joint Angles of Upper Limb Based on sEMG by Using GA-Elman Neural Network

Junhong Wang,Huijiao Wang,Xugang Xi,Jiuwen Cao,Qiqi Hao,Anke Xue

doi:10.1155/2020/4065351

Junhong Wang, Huijiao Wang + Show 4 more

Open Access

https://doi.org/10.1155/2020/4065351

Copy DOI

Abstract

The estimation of continuous and simultaneous multijoint angle based on surface electromyography (sEMG) signal is of considerable significance in rehabilitation practice. However, there are few studies on the continuous joint angle of multiple joints at present. In this paper, the wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal. An Elman neural network optimized by genetic algorithm (GA) was established to estimate the joint angle of shoulder and elbow. First, the accuracy of the method is verified by estimating the angle of the shoulder joint. Then, this method was used to simultaneously and continuously estimate the shoulder and elbow joint angle. Six subjects flexed and extended the upper limbs according to the intended movements of the experiment. The results show that this method can obtain a decent performance with a RMSE of 3.4717 and R2 of 0.8283 in shoulder movement and with a RMSE of 4.1582 and R2 of 0.8114 in continuous synchronous movement of the shoulder and elbow.

Highlights

Hemiplegia is a motor dysfunction caused by nerve damage [1, 2]
We studied the feature method of wavelet packet energy entropy for specific subspaces to alleviate the redundancy problem of adjacent subspaces
Before collecting Surface electromyography (sEMG), the muscle surface of volunteers was wiped with alcohol

Summary

Introduction

Hemiplegia is a motor dysfunction caused by nerve damage [1, 2]. Clinical treatment for hemiplegic patients is mainly treated by one-to-one manual treatment by therapists [3].is approach is time-consuming and cannot be quantified and objectively evaluated. erefore, a new and efficient rehabilitation therapy is urgently needed to make up for the shortcomings of traditional rehabilitation training. Clinical treatment for hemiplegic patients is mainly treated by one-to-one manual treatment by therapists [3]. Is approach is time-consuming and cannot be quantified and objectively evaluated. Erefore, a new and efficient rehabilitation therapy is urgently needed to make up for the shortcomings of traditional rehabilitation training. Robots are used to participate in rehabilitation training, freeing therapists from major physical work, monitoring and evaluating the training process, and developing better rehabilitation programs for patients. Surface electromyography (sEMG) signals can reflect neuromuscular activity to a certain extent, and its collection process is convenient and harmless to the human body. It can adapt to the particularity of the physiological condition of hemiplegic patients. Erefore, it becomes one of the most vital signals that can directly reflect the intended movement of the human body. It is used as a tool to indicate the body’s paralysis of the arm all the time [7]

Methods

Results

Conclusion