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 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.

References:: [1] 于泽升,崔文华,史添玮.基于Kinect手势识别的应用与研究[J].计算机科学,2016,43(增刊2):568-571.
[2] 张凤军,戴国忠,彭晓兰.虚拟现实的人机交互综述[J].中国科学:信息科学,2016,46(12):1711-1736.
[3] SAHA S,LAHIRI R,KONAR A,et al.HMM-based gesture recognition system using kinect sensor for improvised human-computer interaction[C]//2017 International Joint Conference on Neural Networks (IJCNN).New York：IEEE,2017:2776-2783.
[4] 任彧,顾成成.基于HOG特征和SVM的手势识别[J].科技通报,2011,27(2):211-214.
[5] NAIK G R,KUMAR D K,ARJUNAN S P.Towards classification of low-level finger movements using forearm muscle activation:a comparative study based on ICA and Fractal theory[J].International journal of biomedical engineering and technology,2011,6(2):150-162.
[6] 张启忠,席旭刚,罗志增.基于非线性特征的表面肌电信号模式识别方法[J].电子与信息学报,2013,35(9):2054-2058.
[7] LAHIANI H,KHERALLAH M,NEJI M.Hand gesture recognition system based on local binary pattern approach for mobile devices[C]//Intelligent systems design and applications. Berlin:Springer，2018: 619-629.
[8] 曾庆山,宋庆祥,范明莉.基于光流共生矩阵的人群行为异常检测[J].郑州大学学报(工学版),2018,39(3):29-33,39.
[9] 薛俊韬,纵蕴瑞,杨正瓴.基于改进的YCbCr空间及多特征融合的手势识别[J].计算机应用与软件,2016,33(1):151-155.
[10] CHAKRABORTY B K,BHUYAN M K,KUMAR S.Combining image and global pixel distribution model for skin colour segmentation[J].Pattern recognition letters,2017,88:33-40.
[11] TSENG C C,LEE S L.Closed-form designs of digital fractional order Butterworth filters using discrete transforms[J].Signal processing,2017,137:80-97.
[12] 王立玲,杨铮,刘瑾.基于积分肌电值结合双阈值的s-EMG活动段的检测[J].东北师大学报(自然科学版),2018,50(3):65-71.
[13] 王晶晶.多传感器信息融合技术在机器人导航中的应用研究[J].电声技术,2019,43(11):23-24,50.
[14] LU Z Y,CHEN X,LI Q,et al.A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices[J].IEEE transactions on human-machine systems,2014,44(2):293-299.
[15] 王艳丽,梁静,薛冰,等.基于进化计算的特征选择方法研究概述[J].郑州大学学报(工学版),2020,41(1):49-57.
[16] BOTTOU L,CORTES C,DENKER J S,et al.Comparison of classifier methods:a case study in handwritten digit recognition[C]//Proceedings of the 12th IAPR International Conference on Pattern Recognition,Vol.3-Conference C:Signal Processing (Cat.No.94CH3440-5).Piscataway:IEEE,1994:77-82.
[17] KNERR S,PERSONNAZ L,DREYFUS G.Single-layer learning revisited:a stepwise procedure for building and training a neural network[J].Neurocomputing,1990,68:41-50.
[18] PLATT J, CRISTIANINI N, SHAWE-TAYLOR J. Large margin DAG′s for multiclass classification[J]. Neural information processing systems, 2000, 12：547-553.
[19] LU S B,ZHOU L.Fatigue crack monitoring of aerospace structure based on binary tree support vector machines[J].Proceedings of the SPIE,2017,10170:1017032
[20] KUANG H L,CHEN L,GU F,et al.Combining region-of-interest extraction and image enhancement for nighttime vehicle detection[J].IEEE intelligent systems,2016,31(3):57-65.

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