[1]彭金柱、董梦超、杨扬.基于视觉和肌电信息融合的手势识别方法[J].郑州大学学报(工学版),2021,42(02):68-74.[doi:10.13705/j.issn.1671-6833.2021.02.014]
　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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基于视觉和肌电信息融合的手势识别方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年02期
页码:
68-74
栏目:
出版日期:
2021-04-12

文章信息/Info

Title:
Human Gesture Recognition Method Based on Vision and EMG Signal Information
作者:
彭金柱、董梦超、杨扬
郑州大学电气工程学院;
Author(s):
Peng Jinzhu; Dong Mengchao; Yang Yang;
School of Electrical Engineering, Zhengzhou University;
关键词:
Keywords:
gesture recognition;  HOG;  EMG;  time-domain features;  fusion features
DOI:
10.13705/j.issn.1671-6833.2021.02.014
文献标志码:
A
摘要:
针对人机交互技术对手势识别提出的种类多样性和识别精确度的问题，本文提出一种基于视觉方向梯度直方图（HOG）特征和肌电信号（EMG）时域特征的融合及支持向量机（SVM）分类器的手势识别方法。利用视觉传感器和智能臂环分别采集手势图像信息和肌电信号，预处理后提取对应的HOG特征和时域特征；采用串行融合的方式将两种特征进行特征级融合；以SVM为多类分类器完成手势识别模型的训练和检验，并进行对比分析。实验结果表明，采用融合特征的36类手势识别模型的总体正确率达到了96%，能显著减少特征数据量，并有效提高多种类手势识别正确率。
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.

参考文献/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.

更新日期/Last Update: 2021-05-30