Learning Effective Spatial-Temporal Features for sEMG Armband-Based Gesture Recognition

doi:10.1109/JIOT.2020.2979328

	Learning Effective Spatial-Temporal Features for sEMG Armband-Based Gesture Recognition
	Zhang, Yingwei 1,2; Chen, Yiqiang 1,2,3; Yu, Hanchao 1; Yang, Xiaodong 1,2; Lu, Wang 1,2
	2020-08-01
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
卷号	7 期号:8 页码:6979-6992
摘要	Surface electromyography (sEMG) armband-based gesture recognition is an active research topic that aims to identify hand gestures with a single row of sEMG electrodes. As a typical type of biological signal, sEMG on one channel is nonstationary temporally and related to multiple adjacent muscles spatially, which hinders the effective representation in gesture recognition. To tackle these aspects, we propose a spatial-temporal features-based gesture recognition method (STF-GR) in this article. Specifically, STF-GR first decomposes the nonstationary multichannel sEMG by multivariate empirical mode decomposition, which jointly transforms each channel into a series of stationary subsignals. It can keep the temporal stationarity within-channel as well as the spatial independence across-channel. Then, by the convolutional recurrent neural network, STF-GR extracts and merges spatial-temporal features of decomposed sEMG signal. Finally, a negative log-likelihood-based cost function is used to make the final gesture decision. To evaluate the performance of STF-GR, we conduct experiments on three data sets, noninvasive adaptive hand prosthetic (NinaPro), CapgMyo, and BandMyo. The first two are publicly available, and BandMyo is collected by ourselves. Experimental evaluations with within-subject tests show that STF-GR exceeds the performance of other state-of-the-art methods, including deep learning algorithms that are not focused on spatial-temporal features and traditional machine learning algorithms that use handcrafted features.
关键词	Electrodes Gesture recognition Muscles Electromyography Internet of Things Empirical mode decomposition Convolutional recurrent neural network (CRNN) gesture recognition multivariate empirical mode decomposition (MEMD) surface electromyography (sEMG)
DOI	10.1109/JIOT.2020.2979328
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2017YFB1002801] ; Natural Science Foundation of China[61972383] ; Natural Science Foundation of China[61502456] ; Research and Development Plan in Key Field of Guangdong Province[2019B010109001] ; Alibaba Group through the Alibaba Innovative Research Program
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000559482800024
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：29[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15809
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Yiqiang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Peng Cheng Lab, Shenzhen 518066, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yingwei,Chen, Yiqiang,Yu, Hanchao,et al. Learning Effective Spatial-Temporal Features for sEMG Armband-Based Gesture Recognition[J]. IEEE INTERNET OF THINGS JOURNAL,2020,7(8):6979-6992.
APA	Zhang, Yingwei,Chen, Yiqiang,Yu, Hanchao,Yang, Xiaodong,&Lu, Wang.(2020).Learning Effective Spatial-Temporal Features for sEMG Armband-Based Gesture Recognition.IEEE INTERNET OF THINGS JOURNAL,7(8),6979-6992.
MLA	Zhang, Yingwei,et al."Learning Effective Spatial-Temporal Features for sEMG Armband-Based Gesture Recognition".IEEE INTERNET OF THINGS JOURNAL 7.8(2020):6979-6992.