Improving sEMG-Based Hand Gesture Recognition through Optimizing Parameters and Sliding Voting Classifiers

Abstract

In this paper, we present a preliminary study that proposes to improve surface electromyography (sEMG)-based hand gesture recognition through optimizing parameters and sliding voting classifiers. Targeting the high-performing myoelectric control system, the traditional methods for hand gesture recognition still need to further improve the classification accuracy and utilization rate for sEMG signals. Therefore, the proposed method first optimizes parameters to reduce redundant information by selecting the proper values for the window length, the overlapping rate, the number of channels, and the features of sEMG signals. In addition, the random forest (RF) classifier is an advanced classifier for sEMG-based hand gesture recognition. To further improve classification performance, this paper proposes a sliding voting random forest (SVRF) classifier which can reduce potential pseudo decisions made by the RF classifier. Finally, experiments were conducted using two sEMG datasets, named DB2 and DB4, from the NinaPro database, as well as self-collected data. The results illustrate a certain improvement in classification accuracy based on the optimized values for window length, overlapping rate, number of channels, and features of sEMG signals. And the SVRF classifier can significantly improve performance with higher accuracy compared with the traditional linear discriminate analysis (LDA), k-nearest neighbors (KNN), support vector machine (SVM), and RF classifiers.