Surface EMG based handgrip force predictions using gene expression programming

The main objective of this study is to precisely predict muscle forces from surface electromyography (sEMG) for hand gesture recognition. A robust variant of genetic programming, namely Gene Expression Programming (GEP), is utilized to derive a new empirical model of handgrip sEMG–force relationship. A series of handgrip forces and corresponding sEMG signals were recorded from 6 healthy male subjects and during 4 levels of percentage of maximum voluntary contraction (%MVC) in experiments. Using one-way ANOVA with multiple comparisons test, 10 features of the sEMG time domain were extracted from homogeneous subsets and used as input vectors. Subsequently, a handgrip force prediction model was developed based on GEP. In order to compare the performance of this model, other models based on a back propagation neural network and a support vector machine were trained using the same input vectors and data sets. The root mean square error and the correlation coefficient between the actual and predicted forces were calculated to assess the performance of the three models . The results show that the GEP model provide the highest accuracy and generalization capability among the studied models. It was concluded that the proposed GEP model is relatively short, simple and excellent for predicting handgrip forces based on sEMG signals.