Paper Info
Title
Multi-object intergroup gesture recognition combined with fusion feature and KNN algorithm.
Abstract
SEMG signal is a bioelectrical signal produced by the contraction of human surface muscles. Human-computer interaction based on SEMG signal is of great significance in the field of rehabilitation robots. In this study, a feature extraction method of SEMG signal based on activated muscle regionis proposed, which is based on the study of activated muscle regionin human forearm and hand movement. At the same time, the main research object of this study is the multi-object intergroup SEMG signal which is closer to the practical application environment. The new feature extracted is fused with the sample entropy feature and the wavelength feature to obtain better signal features. After combining the fusion feature with KNN algorithm, the hand motion pattern recognition and classification between multi-object groups is carried out. The combination of the fusion feature and KNN classification algorithm can achieve 91.05% in the multi-object intergroup hand motion classification. This method has lower computational cost without expensive hardware support, and improves the robustness of hand motion recognition based on EMG signals.
Year
DOI
Venue
2020
10.3233/JIFS-179558
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
DocType
Volume
Gesture recognition,EMG signal,activated muscle region,feature extraction
Journal
38
Issue
ISSN
Citations 
SP3.0
1064-1246
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Shangchun Liao100.34
Gongfa Li223943.45
Jiahan Li391.82
Du Jiang49714.40
Guozhang Jiang517227.25
Ying Sun629140.03
Bo Tao700.34
Haoyi Zhao800.68
Disi Chen9397.70