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

Electronics (Basel), 2024-04, Vol.13 (7), p.1322 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2079-9292 ;EISSN: 2079-9292 ;DOI: 10.3390/electronics13071322

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  • Title:
    Improving sEMG-Based Hand Gesture Recognition through Optimizing Parameters and Sliding Voting Classifiers
  • Author: Zhang, Ming ; Liu, Shizhao ; Li, Xiao ; Qu, Leyi ; Zhuang, Bowen ; Han, Gujing
  • Subjects: Accuracy ; Analysis ; Channels ; Classification ; Classifiers ; Control systems ; Datasets ; Decision trees ; Discriminant analysis ; Electromyography ; Gesture recognition ; hand gesture recognition ; Methods ; Myoelectric control ; Myoelectricity ; Parameters ; Performance enhancement ; sEMG signal ; Sliding ; sliding voting classifier ; sliding window ; Support vector machines ; time-domain features ; Voting
  • Is Part Of: Electronics (Basel), 2024-04, Vol.13 (7), p.1322
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2079-9292
    EISSN: 2079-9292
    DOI: 10.3390/electronics13071322
  • Source: Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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