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A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control
Geng, Yanjuan1,2; Ouyang, Yatao3; Samuel, Oluwarotimi Williams1,2; Chen, Shixiong1,2; Lu, Xiaoqiang4; Lin, Chuang1,2; Li, Guanglin1,2
Department光学影像学习与分析中心
2018
Source PublicationIEEE ACCESS
ISSN2169-3536
Volume6Pages:38326-38335
Contribution Rank4
Abstract

Interferences in the form of white Gaussian noise (WGN) are inevitable during long-term electromyogram (EMG) recordings. Even with the aid of advanced signal denoising techniques, such an intermittent interference is hardly detected and attenuated in the practical use of EMG-driven control systems. Hence, a robust pattern recognition scheme that is invariant to noise contamination would potentially aid the realization of an efficient EMG-based pattern recognition (EMG-PR) control system. To this end, an EMG-PR scheme driven by sparse representation-based classification (SRC) algorithm and root mean square (rms) descriptor (RMS-SRC) is proposed in this paper. The accuracy and the robustness of the proposed scheme were investigated using the high-density surface EMG recordings from 12 traumatic brain-injured patients and 5 post-stroke survivors. For benchmark comparison, another three different feature sets and four pattern recognition algorithms were considered. The optimal pattern recognition schemes with respect to each feature-classifier combination were first selected in the absence of WGN contamination. Then, six levels of WGN with a signal-to-noise ratio (SNR) ranging from 5 to 30 dB were added into the EMG recordings, respectively, to mimic the different WGN interferences. Our result showed that the proposed RMS-SRC scheme could achieve a similar accuracy with the benchmark schemes in the presence of limited noise contamination (0-15 dB), and significantly outperformed the other schemes when the SNR of WGN increased (20-30 dB). More notably, the RMS-SRC scheme significantly outperformed the other pattern recognition schemes when the WGN existed in either training set or testing set only. The findings proved the comparative advantage of the proposed RMS-SRC pattern recognition scheme over the other currently used schemes in the myoelectric control. Thus, the proposed scheme would potentially facilitate the development of EMG-driven rehabilitation robots for accurate and dexterous assistive training for patients with neurological disorders.

KeywordElectromyogram Pattern Recognition Sparse Representation Classifier Robustness White Gaussian Noise
DOI10.1109/ACCESS.2018.2851282
Indexed BySCI ; EI
Language英语
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS IDWOS:000440541700001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20182705408808
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30555
Collection光学影像学习与分析中心
Affiliation1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
2.Chinese Acad Sci, Key Lab Human Machine Intelligence Synergy Syst, Shenzhen 518055, Peoples R China
3.Guangdong Prov Work Injury Rehabil Ctr, Guangzhou 510440, Guangdong, Peoples R China
4.Chinese Acad Sci, Xian Inst Opt & Preci Mech, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Geng, Yanjuan,Ouyang, Yatao,Samuel, Oluwarotimi Williams,et al. A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control[J]. IEEE ACCESS,2018,6:38326-38335.
APA Geng, Yanjuan.,Ouyang, Yatao.,Samuel, Oluwarotimi Williams.,Chen, Shixiong.,Lu, Xiaoqiang.,...&Li, Guanglin.(2018).A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control.IEEE ACCESS,6,38326-38335.
MLA Geng, Yanjuan,et al."A Robust Sparse Representation Based Pattern Recognition Approach for Myoelectric Control".IEEE ACCESS 6(2018):38326-38335.
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