A Parallel Network for Continuous Motion Estimation of Finger Joint Angles with Surface Electromyographic Signals

The implementation of surface electromyographic (sEMG) signals in the interaction between human beings and machines is an important line of research. In the system of human–machine interaction, continuous-motion-estimation-based control plays an important role because it is more natural and intuitive than pattern recognition-based control. In this paper, we propose a parallel network consisting of a CNN with a multi-head attention mechanism and a BiLSTM (bidirectional long short-term memory) network to improve the accuracy of continuous motion estimation. The proposed network is evaluated in the Ninapro dataset. Six finger movements of 10 subjects were tested in the Ninapro DB2 dataset to evaluate the performance of the neural network and calculate the PCC (Pearson Correlation Coefficient) between the predicted joint angle sequence and the actual joint angle sequence. The experimental results show that the average accuracy (PCC) of the proposed network reaches 0.87 ± 0.02, which is significantly better than that of the BiLSTM network (0.79 ± 0.04, p < 0.05), CNN-Attention (0.80 ± 0.01, p < 0.05), CNN (0.70 ± 0.03, p < 0.05), CNN-BiLSTM (0.83 ± 0.02, p < 0.05), and TCN (0.76 ± 0.05, p < 0.05). It is worth noting that in this work, we extract multiple features from the raw sEMG signals and fuse them. We found that better continuous estimation accuracy can be achieved using multi-feature sEMG data. The model proposed in this paper skillfully integrates the convolutional neural network, multi-head attention mechanism, and bidirectional long short-term memory network, and its performance has good stability and accuracy. The model realizes more natural and accurate human–computer interaction.