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SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

文献类型：期刊论文

作者She, Haotian3,4; Zhu, Jinying1,5; Tian, Ye3,4; Wang, Yanchao3,4; Yokoi, Hiroshi1,2; Huang, Qiang3,4
刊名SENSORS
出版日期2019-10-02
卷号19期号:20页码:15
关键词surface EMG signal feature extraction Stockwell transform hand movement recognition
DOI10.3390/s19204457
通讯作者Zhu, Jinying(zhujinying01@163.com) ; Tian, Ye(tianye7248@bit.edu.cn)
英文摘要Feature extraction, as an important method for extracting useful information from surface electromyography (SEMG), can significantly improve pattern recognition accuracy. Time and frequency analysis methods have been widely used for feature extraction, but these methods analyze SEMG signals only from the time or frequency domain. Recent studies have shown that feature extraction based on time-frequency analysis methods can extract more useful information from SEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwell transform (S-transform) to improve hand movement recognition accuracy from forearm SEMG signals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vector from forearm SEMG signals. Second, to reduce the amount of calculations and improve the running speed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of the feature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is used for recognizing hand movements. Experimental results show that the proposed feature extraction based on the S-transform analysis method can improve the class separability and hand movement recognition accuracy compared with wavelet transform and power spectral density methods.
WOS关键词EMG PATTERN-RECOGNITION ; SURFACE EMG ; PROSTHETIC HANDS ; FEATURE-PROJECTION ; CLASSIFICATION ; WAVELET ; RECORDINGS ; SIGNALS ; SCHEME ; ROBUST
资助项目Beijing Advanced Innovation Center of Intelligent Robots and Systems[2016IRS23]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000497864700105
出版者MDPI
资助机构Beijing Advanced Innovation Center of Intelligent Robots and Systems
源URL[http://ir.ia.ac.cn/handle/173211/29377]  
专题精密感知与控制研究中心_精密感知与控制
通讯作者Zhu, Jinying; Tian, Ye
作者单位1.Beijing Adv Innovat Ctr Intelligent Robot & Syst, Beijing 100081, Peoples R China
2.Univ Electrocommun, Sch Informat & Engn, Tokyo 1638001, Japan
3.Beijing Inst Technol, Sch Mechatron Engn, Beijing 100081, Peoples R China
4.Minist Educ, Key Lab Biomimet Robots & Syst, Beijing 100081, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 She, Haotian,Zhu, Jinying,Tian, Ye,et al. SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy[J]. SENSORS,2019,19(20):15.
APA She, Haotian,Zhu, Jinying,Tian, Ye,Wang, Yanchao,Yokoi, Hiroshi,&Huang, Qiang.(2019).SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy.SENSORS,19(20),15.
MLA She, Haotian,et al."SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy".SENSORS 19.20(2019):15.

入库方式： OAI收割

来源：自动化研究所

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