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Electrode shifts estimation and adaptive correction for improving robustness of sEMG-based recognition
Li ZY(李自由)1,2,3; Zhao XG(赵新刚)1,2; Liu GJ(刘光军)4; Zhang B(张弼)1,2; Zhang DH(张道辉)1,2; Han JD(韩建达)5
Department机器人学研究室
Source PublicationIEEE Journal of Biomedical and Health Informatics
ISSN2168-2194
2021
Volume25Issue:4Pages:1101-1110
Indexed BySCI ; EI
EI Accession number20211610218026
WOS IDWOS:000638401400020
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China under Grants U1813214, 61903360, 6177369, and 61821005 ; China Postdoctoral Science Foundation under Grant 2019M661155
KeywordSurface electromyography (sEMG) polar coordinate system electrode shifts adaptive correction pattern recognition
Abstract

In sEMG-based recognition systems, accuracy is severely worsened by disturbances, such as electrode shifts by doffing/donning. Traditional recognition models are fixed or static, with limited abilities to work in the presence of the disturbances. In this paper, a transfer learning method is proposed to reduce the impact of electrode shifts. In the proposed method, a novel activation angle is introduced to locate electrodes within a polar coordinate system. An adaptive transformation is utilized to correct electrode-shifted sEMG samples. The transformation is based on estimated shifts relative to the initial position. The experiments acquisition data from ten subjects consist of sEMG signals under eight gestures in seven or nine arbitrary positions, and recorded shifts from a 3D-printed annular ruler. In our extensive experiments, the errors between recorded shifts (as the reference) and estimated shifts is about -0.017± 0.13 radians. Eight gestures recognition results have shown an average accuracy around 79.32%, which represents a significant improvement over the 35.72% (p (p < 0.0001) average accuracy of results obtained using nonadaptive models, and 60.99% (p < 0.0001) results of the other method iGLCM (an improved gray-level co-occurrence matrix). More importantly, by only using one-label samples, the proposed method updates the pre-trained model in an initial position. As a result, the pre-trained model can be adaptively corrected to recognize eight-label gestures in arbitrarily rotary positions. It is proven a highly efficient way to relieve subjects' re-training burden of sEMG-based rehabilitation systems.

Language英语
WOS SubjectComputer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS Research AreaComputer Science ; Mathematical & Computational Biology ; Medical Informatics
Funding ProjectNational Natural Science Foundation of China[U1813214] ; National Natural Science Foundation of China[61903360] ; National Natural Science Foundation of China[6177369] ; National Natural Science Foundation of China[61821005] ; China Postdoctoral Science Foundation[2019M661155]
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28732
Collection机器人学研究室
Corresponding AuthorZhao XG(赵新刚)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.The Institutes for Robotics, and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.The University of Chinese Academy of Sciences, Beijing 100049, China
4.Department of Aerospace Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
5.Institute of Robotics, and Automatic Information Systems, College of Artificial Intelligence, The Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
Recommended Citation
GB/T 7714
Li ZY,Zhao XG,Liu GJ,et al. Electrode shifts estimation and adaptive correction for improving robustness of sEMG-based recognition[J]. IEEE Journal of Biomedical and Health Informatics,2021,25(4):1101-1110.
APA Li ZY,Zhao XG,Liu GJ,Zhang B,Zhang DH,&Han JD.(2021).Electrode shifts estimation and adaptive correction for improving robustness of sEMG-based recognition.IEEE Journal of Biomedical and Health Informatics,25(4),1101-1110.
MLA Li ZY,et al."Electrode shifts estimation and adaptive correction for improving robustness of sEMG-based recognition".IEEE Journal of Biomedical and Health Informatics 25.4(2021):1101-1110.
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