A novel sEMG-based force estimation method using deep-learning algorithm
Shaoyang Hua, Congqing Wang, Xuewei Wu
Abstract
Abstract 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.