CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
A Novel Feature Reduction Method for Real-Time EMG Pattern Recognition System
Yang, Peipei1; Xing, Kexin2; Huang, Jian3; Wang, Yongji3
2013-05
Conference NameChinese Control and Decision Conference (CCDC)
Source Publication2013 25th Chinese Control and Decision Conference (CCDC)
Conference Date2013-5-25
Conference PlaceGuiyang, China
AbstractThis paper proposes a novel feature reduction approach for real-time electromyogram (EMG) pattern recognition. This study extracts time and frequency information by wavelet packet transform (WPT) coefficients and uses the node energy as the feature to overcome the translation-invariant property of WPT. Then the non-parametric discriminant analysis (NDA) is used for feature reduction. Because of some inherent properties of the packet node energy, the within-class scatter matrix is usually singular in this approach, which makes feature project unavailable. To solve this problem, a recursive algorithm is proposed to discard some feature components that lead to singularity and contain relatively less discriminant information. Finally, the support vector machine (SVM) is used as the classifier and gives the recognition result. The corresponding pattern of the action could be recognized in a millisecond (ms). The experimental results show that the proposed method has strong robustness and good real-time performance.
KeywordEmg Real-time Pattern Recognition Wavelet Packet Non-parametric Weighted Feature Extraction Svm
DOI10.1109/CCDC.2013.6561165
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12502
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorYang, Peipei
Affiliation1.Institute of Automation, Chinese Academy of Science
2.College of Information Engineering, Zhejiang University of Technology
3.Huazhong University of Science and Technology
Recommended Citation
GB/T 7714
Yang, Peipei,Xing, Kexin,Huang, Jian,et al. A Novel Feature Reduction Method for Real-Time EMG Pattern Recognition System[C],2013.
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