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 of 3.4717 and of 0.8283 in shoulder movement and with a of 4.1582 and of 0.8114 in continuous synchronous movement of the shoulder and elbow.

中文翻译：

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GA-Elman神经网络基于sEMG的上肢连续关节角度估计

基于表面肌电图（sEMG）信号的连续和同时多关节角的估计在康复实践中具有重要意义。但是，目前关于多关节的连续关节角度的研究很少。本文研究了特殊子空间的小波包能量熵（WPEE）作为sEMG信号的特征。建立了通过遗传算法（GA）优化的Elman神经网络，以估计肩膀和肘部的关节角度。首先，通过估计肩关节角度来验证该方法的准确性。然后，该方法用于同时连续地估计肩和肘关节角度。六名受试者根据实验的预期动作弯曲并伸展了上肢。的3.4717和在肩部运动的0.8283和带的4.1582和在肩部和肘的连续同步运动的0.8114。