Novel hybrid soft computing pattern recognition system SVM–GAPSO for classification of eight different hand motions

Abstract

In this manuscript, we proposed an intelligent hybrid pattern recognition system to classify eight hand motions. Two evolutionary algorithms: genetic algorithm (GA) and particle swarm optimization (PSO) were combined with support vector machine (SVM) to construct a novel model (GAPSO–SVM). The proposed hybrid system able to obtain a high performance in handling learning task with SVM by selecting the optimal parameters of radial basis function (RBF–SVM) and choosing the optimal decomposition level of the mother wavelet function. In this study, energy of wavelet coefficients (EWC) were extracted from surface electromyographic (sEMG) signals, which collected from forearm's muscles. Moreover, due to the reason that the effectiveness of myoelectric pattern recognition system also depends on the degree of dimensionality reduction of the selected feature vector (FV), we investigated the performance of different feature projection algorithms: neighborhood preserving embedding (NPE), principle component analysis (PCA), independent components analysis (ICA) and orthogonal linear discriminant analysis (OLDA). The result shows that the proposed hybrid model predicts with greater accuracy and reliability in comparison to other three classifiers: support vector machines (SVM), fuzzy least squares support vector machines (LS–SVM) and K-nearest neighbor (K-NN). The highest achieved average accuracy rate was 98.7% by employing SVM-GAPSO classifier based on PCA.