This paper discusses the problem of force estimation represented by surface electromyography (sEMG) signals collected from an armband-like collection device. The scheme is proposed for the sake of two dimensions of sEMG signals: spatial and temporal information. From the point of space, first, appropriate channel number across all subjects is investigated. During this progress, an electrode channel selection method based on Spearman’s rank order correlation coefficient is utilized to detect signals from active muscle. Then, to reduce the computation and highlight the channel information, linear regression (LR) algorithm is conducted to weight each channel. Besides, the recurrent neural network (RNN) is used to capture the temporal information and model the relation between sEMG and output force. Experiments conducted on four subjects demonstrate that six channels are enough to characterize the muscle activity. By combining the selected channels with different weight coefficients, LR algorithm can fit the output force better than simply averaging them. Furthermore, RNN with long short-term memory cell shows the superiority in time series modeling, which can improve our results to a greater degree. Experimental results prove the feasibility of the proposed method.

本文讨论了从臂章式收集装置收集的表面肌电图（sEMG）信号表示的力估计问题。提出该方案是出于sEMG信号的两个维度：空间和时间信息。从空间的角度出发，首先，研究所有主题之间的适当频道号。在此过程中，利用基于Spearman秩相关系数的电极通道选择方法来检测来自活动肌肉的信号。然后，为了减少计算量并突出显示频道信息，进行了线性回归（LR）算法以加权每个频道。此外，还使用递归神经网络（RNN）捕获时间信息并建模sEMG与输出力之间的关系。在四个对象上进行的实验表明，六个通道足以表征肌肉活动。通过组合选定的具有不同权重系数的通道，LR算法比简单地对它们进行平均可以更好地拟合输出力。此外，具有长短期记忆单元的RNN在时间序列建模方面显示出优越性，这可以在更大程度上改善我们的结果。实验结果证明了该方法的可行性。