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A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG

Authors :
Yuxuan Wang
Ye Tian
Jinying Zhu
Haotian She
Yinlai Jiang
Zhihong Jiang
Hiroshi Yokoi
Source :
Cyborg and Bionic Systems, Vol 5 (2024)
Publication Year :
2024
Publisher :
American Association for the Advancement of Science (AAAS), 2024.

Abstract

The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition. However, as the number of acquisition channels increases, its effect on the improvement of the accuracy of gesture recognition gradually diminishes, resulting in the improvement of the control effect reaching a plateau. To address these problems, this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels. This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels, ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings. Meanwhile, based on the idea of the filtered feature selection method, a quantitative measure of sample sets (separability of feature vectors [SFV]) derived from the divergence and correlation of the extracted features is introduced. The SFV value can predict the classification effect before performing the classification, and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change. Compared to the statistical motion intention recognition success rate, SFV is a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.

Subjects

Subjects :
Cybernetics
Q300-390

Details

Language :
English
ISSN :
26927632
Volume :
5
Database :
Directory of Open Access Journals
Journal :
Cyborg and Bionic Systems
Publication Type :
Academic Journal
Accession number :
edsdoj.f7d73e73558544e7b84085fe60cbfa2c
Document Type :
article
Full Text :
https://doi.org/10.34133/cbsystems.0066