Optimization of sEMG Classification Model Based on Correlation Analysis and Feature Selection

This paper devoted to the optimization of surface electromyography (sEMG) based hand gesture recognition classifier model. Twenty-two features in time domain, frequency domain and time-frequency domain were extracted from sEMG of 6 muscles to classify 13 hand gestures. Correlation analysis was used to screen the features. Random forest (RF) and principal component analysis (PCA) feature selection were used to further reduce the feature dimensions. Finally, mean absolute value (MAV), root mean square (RMS), slope sign change (SSC), mean frequency (MNF), Willison amplitude (WAMP) and autoregressive coefficients (AR1), totally 6 features were selected. LDA (Linear Discriminant Analysis), SVM (Support Vector Machine), KNN (k-Nearest Neighbor), DT (Decision Tree) and NN (Neural Network), in total of five classification methods were applied. The accuracy of the classifier had no significant reduction, but the dimension of the feature set was reduced from 132 to 36, and the training time was greatly reduced. The result indicated that the combination of correlation analysis and random forest feature selection can optimize the classifier model and greatly reduce the training time of the classifier.