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

Abstract EN

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.