Human Gesture Recognition Method Based on Vision and EMG Signal Information

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Human Gesture Recognition Method Based on Vision and EMG Signal Information

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[1]Peng Jinzhu,Dong Mengchao,Yang Yang,et al.Human Gesture Recognition Method Based on Vision and EMG Signal Information[J].Journal of Zhengzhou University (Engineering Science),2021,42(02):68-74.[doi:10.13705/j.issn.1671-6833.2021.02.014]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 02 Page number: 68-74 Column: Public date: 2021-04-12

Title:: Human Gesture Recognition Method Based on Vision and EMG Signal Information

Author(s):: Peng Jinzhu; Dong Mengchao; Yang Yang;; School of Electrical Engineering, Zhengzhou University;

Keywords:: gesture recognition; HOG; EMG; time-domain features; fusion features

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.02.014

Abstract:: Aiming at the problem of variety and accuracy of gesture recognition in human-computer interaction, a gesture recognition method was proposed based on fusion features of visual histogram of orientation gradient (HOG) and time-domain features of electromyography (EMG), where support vector machine (SVM) was used as a classifier. Visual sensors and smart armbands were used to collect gesture image information and EMG signals respectively, and the corresponding HOG features and time domain features were then extracted after preprocessing. These two features were fused at feature level by employing serial fusion. The combined SVM multiple classifier constructed by one Vs one was used to train and verify the gesture recognition model. Experimental results showed that the overall accuracy of 36 types of gesture recognition models reached 96% by using the proposed fusion features, which is 33% and 16% higher than the single hog feature and EMG time-domain feature before fusion, respectively. Compared with decision-level fusion, the accuracy of the proposed method was increased by 11%, and the calculation time was only 0.274 ms, which could effectively reduce the amount of feature data and significantly improve the accuracy of multiple types of gesture recognition.

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