Classification of surface electromyogram signals based on directed acyclic graphs and support vector machines

Abstract

This paper presents a novel classification approach for surface electromyogram (sEMG) signals. The proposedclassification approach involves two steps: (1) feature extraction from an sEMG, in which a 7-dimensional featurevector is extracted from 27 types of features of the sEMG by linear discriminant analysis (LDA), and (2) a novelclassifier, DAGSVMerr, based on a directed acyclic graph (DAG) and support vector machine (SVM), in which aseparability measure function based on erroneous recognition rates (ERRs) is defined to determine the initial operationlist. The proposed approach takes advantage of the feedback idea to improve the performance of the classification. Theexperimental results show that the proposed approach has a better performance than traditional methods, and it achievesan average classification accuracy rate of 99.4% ± 1.3% with an error rate of 0.6%. Correct classification rates of theproposed approach are very high, and the approach can be utilized to recognize gesture instructions by analyzing sEMGsignals in gesture equipment control studies.