Cross-Comparison of EMG-to-Force Methods for Multi-DoF Finger Force Prediction Using One-DoF Training

Surface electromyography (sEMG) signal is one of the widely applied biological signals in the research field of the force intention prediction. However, due to the severe cross-talk issue of sEMG signals during fine hand contractions, few studies have related sEMG to multiple degree-of-freedom (DoF) force prediction of individual fingers simultanously. Accordingly, this study proposed methods mainly based on neural networks: Convolutional neural Network (CNN) and Recurrent Neural Network (RNN) to achieve better prediction results. Several improvements on traditional methods are also proposed in this article such as: Common Spatial Pattern (CSP), Softmax function and several new channel selection standards to solve the cross-talk issues for the estimation of EMG-force during multiple finger contractions. High-density sEMG signals of forearm extensor muscles were obtained, and experimental data from seven able-bodied subjects were analyzed. Subjects produced 1-DoF and Multi-DoF forces up to 30 & x0025; maximum voluntary contraction (MVC). Then, the root-mean-square values of sEMG were related to joint force. To realize a better practical use, the EMG-to-force models were trained with minimal numbers of trials (using 1-DoF trials only), then assessed on multi-DoF trials. Our results showed that the proposed modifications on traditional method also made an improvement on the prediction results. Our findings suggest that Multi-DoF control for individual fingers with minimal training procedure (using 1-DoF trials only) may be feasible for practical use. Furthermore, methods based on neural networks greatly outperform traditional methods and the combination of CNN and LSTM showed the best performance.