| 基于递归量化分析的表面肌电特征提取和分类 |
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| 引用本文： | 尹少华,杨基海,梁政,陈香,任焱暄.基于递归量化分析的表面肌电特征提取和分类[J].中国科学技术大学学报,2006,36(5):550-555. |
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| 作者姓名： | 尹少华 杨基海 梁政 陈香 任焱暄 |
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| 作者单位： | 1. 中国科学技术大学电子科学与技术系,安徽,合肥,230026
2. 安徽移动通信有限责任公司,安徽,合肥,230000 |
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| 摘 要： | 利用展拳、握拳和腕屈、腕伸时从前臂分别检测的两路表面肌电（surface electromyography,SEMG）信号,对四种动作进行了分类研究.先采用移动平均法（moving average,MA）和一阶差分法确定SEMG信号中对应的每个动作波形的起止点,再利用递归量化分析（recurrence quantification analysis,RQA）方法提取各种动作波形的非线性特征参量（确定率、递归率等）,由两路SEMG信号的这些特征参量构成特征矢量,输入BP（back propagation）神经网络,完成对不同动作的分类.研究结果表明,将利用递归量化分析得到SEMG信号的几种非线性参量作为特征值,对不同动作进行分类能够获得较高的分类准确率.
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| 关 键 词： | 表面肌电信号 递归量化分析 移动平均 一阶差分 神经网络 模式分类 |
| 文章编号： | 0253-2778(2006)05-0550-06 |
| 收稿时间： | 06 20 2005 12:00AM |
| 修稿时间： | 02 27 2006 12:00AM |
Recurrence quantification analysis based on surface EMG signal feature extraction and classification |
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| YIN Shao-hua,YANG Ji-hai,LIANG Zheng,CHEN Xiang,REN Yan-xuan.Recurrence quantification analysis based on surface EMG signal feature extraction and classification[J].Journal of University of Science and Technology of China,2006,36(5):550-555. |
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| Authors: | YIN Shao-hua YANG Ji-hai LIANG Zheng CHEN Xiang REN Yan-xuan |
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| Institution: | 1. Department of Electronic Science and Technology, USTC , Hefei 230026,China ; 2. Anhui Mobile Communication Co. , Ltd. , Hefei 230000,China |
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| Abstract: | A new method for surface electromyography(SEMG) signal feature extraction based on recurrence quantification analysis(RQA) was proposed.The classification performance of four types of forearm movement using two channel SEMG signals from the forearm was researched.The classical moving average technique and first order differential were used for segmentation.Recurrence quantification analysis was adopted as an effective feature extraction technique while artificial neural networks were used for classification.Results of classifying four types of forearm movement signals gave a higher identification rate. |
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| Keywords: | surface EMG recurrence quantification analysis moving average first order differential neural network pattern classification |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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