Institute of Computing Technology, Chinese Academy IR
Learning Effective Spatial-Temporal Features for sEMG Armband-Based Gesture Recognition | |
Zhang, Yingwei1,2; Chen, Yiqiang1,2,3; Yu, Hanchao1; Yang, Xiaodong1,2; Lu, Wang1,2 | |
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 | |
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. |
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