[1]冯凯,董秀成,刘栋博.基于经验模态分解-小波包变换的表面肌电信号手势识别[J].中国医学物理学杂志,2021,38(4):461-467.[doi:DOI:10.3969/j.issn.1005-202X.2021.04.013]
　FENG Kai,DONG Xiucheng,LIU Dongbo.Empirical mode decomposition and wavelet packet transform applied to surface EMG signal for hand gesture recognition[J].Chinese Journal of Medical Physics,2021,38(4):461-467.[doi:DOI:10.3969/j.issn.1005-202X.2021.04.013]
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基于经验模态分解-小波包变换的表面肌电信号手势识别()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第4期
页码:
461-467
栏目:
医学信号处理与医学仪器
出版日期:
2021-04-29

文章信息/Info

Title:
Empirical mode decomposition and wavelet packet transform applied to surface EMG signal for hand gesture recognition
文章编号:
1005-202X（2021）04-0461-07
作者:
冯凯; 董秀成; 刘栋博
西华大学电气与电子信息学院， 四川 成都 611730
Author(s):
FENG Kai;  DONG Xiucheng;  LIU Dongbo
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 611730, China
关键词:
表面肌电信号; 经验模态分解; 小波包变换; 特征提取; 模式识别
Keywords:
Keywords: surface electromyography signal empirical mode decomposition wavelet packet transformation feature extraction pattern recognition
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2021.04.013
文献标志码:
A
摘要:
为了提高表面肌电信号（sEMG）手部运动识别的正确率，比较常规的sEMG预处理和特征提取方法，提出一种基于经验模态分解（EMD）和小波包变换（WPT）的sEMG手势识别模型。首先，使用EMD方法将sEMG进行平稳化，得到一系列的固有模态函数。其次，求取各个固有模态函数与原始信号的相关性，选取相关性较高的前4个分量作为有效分量。然后，采用Db3小波函数进行WPT，提取小波包系数中的平均能量、平均绝对值、最大值、均方根和方差等特征。分别采用线性判别分析和支持向量机对12种手部运动进行模式识别。结果表明基于EMD和WPT的sEMG手势识别正确率比直接提取小波包系数中的特征识别正确率高。
Abstract:
Abstract: Compared with conventional surface electromyography (EMG) signal preprocessing and feature extraction methods, a model based on empirical mode decomposition and wavelet packet transform for hand gesture recognition through surface EMG signals is proposed for improving the accuracy of surface EMG signal in hand gesture recognition. Empirical mode decomposition is firstly used to smooth the surface EMG signal for obtaining a series of intrinsic mode functions. Subsequently, the correlation between each intrinsic mode function and the original signal is obtained, and the top 4 components with higher correlation are selected as effective components. Then, Db3 wavelet function is used to perform wavelet packet transformation, and the average energy, average absolute value, maximum value, root mean square and variance of the wavelet packet coefficients are extracted. Finally, linear discriminant analysis and support vector machine are used to recognize 12 hand gestures separately. The results show that the hand gesture recognition accuracy of applying empirical mode decomposition and wavelet packet transform to surface EMG signal is higher than that of directly extracting wavelet packet coefficients.

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备注/Memo

备注/Memo:
【收稿日期】2020-10-15 【基金项目】国家自然科学基金青年科学基金(61901393)；四川省科技厅重点项目（2018JY0463）；教育部“春晖计划”科研项目（Z2017076）；四威高科—西华大学产学研联合实验室（2016-YF04-00044-JH） 【作者简介】冯凯，硕士研究生，研究方向：模式识别，E-mail: fk6817@163.com 【通信作者】董秀成，教授，硕士生导师，研究方向：智能信息处理、机器视觉，E-mail: dxc136@163.com
更新日期/Last Update: 2021-04-29