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

doi:10.3390/s19204457

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 精密感知与控制

	SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy
	She, Haotian 1,2; Zhu, Jinying 3,4; Tian, Ye1,2 ; Wang, Yanchao 1,2; Yokoi, Hiroshi 4,5; Huang, Qiang1,2
发表期刊	SENSORS
	2019-10-02
卷号	19 期号:20 页码:15
通讯作者	Zhu, Jinying(zhujinying01@163.com) ; Tian, Ye(tianye7248@bit.edu.cn)
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.
关键词	surface EMG signal feature extraction Stockwell transform hand movement recognition
DOI	10.3390/s19204457
关键词[WOS]	EMG PATTERN-RECOGNITION ; SURFACE EMG ; PROSTHETIC HANDS ; FEATURE-PROJECTION ; CLASSIFICATION ; WAVELET ; RECORDINGS ; SIGNALS ; SCHEME ; ROBUST
收录类别	SCI
语种	英语
资助项目	Beijing Advanced Innovation Center of Intelligent Robots and Systems[2016IRS23] ; Beijing Advanced Innovation Center of Intelligent Robots and Systems[2016IRS23]
项目资助者	Beijing Advanced Innovation Center of Intelligent Robots and Systems
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000497864700105
出版者	MDPI
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/29378
专题	中科院工业视觉智能装备工程实验室_精密感知与控制
通讯作者	Zhu, Jinying; Tian, Ye
作者单位	1.Beijing Inst Technol, Sch Mechatron Engn, Beijing 100081, Peoples R China 2.Minist Educ, Key Lab Biomimet Robots & Syst, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Beijing Adv Innovat Ctr Intelligent Robot & Syst, Beijing 100081, Peoples R China 5.Univ Electrocommun, Sch Informat & Engn, Tokyo 1638001, Japan
通讯作者单位	中国科学院自动化研究所
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.