Knowledge Commons of Institute of Automation,CAS
SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy | |
She, Haotian1,2; Zhu, Jinying3,4; Tian, Ye1,2; Wang, Yanchao1,2; Yokoi, Hiroshi4,5; 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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29378 |
专题 | 中国科学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. |
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