Iterative Self-Training Based Domain Adaptation for Cross-User sEMG Gesture Recognition

doi:10.1109/TNSRE.2023.3293334

	Iterative Self-Training Based Domain Adaptation for Cross-User sEMG Gesture Recognition
	Wang, Kang 1,2; Chen, Yiqiang 2,3,4,5; Zhang, Yingwei 2,3,4,5; Yang, Xiaodong 2,3,4,5; Hu, Chunyu 1
	2023
发表期刊	IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
ISSN	1534-4320
卷号	31 页码:2974-2987
摘要	Surface electromyography (sEMG) based gesture recognition has received broad attention and application in rehabilitation areas for its direct and fine-grained sensing ability. sEMG signals exhibit strong user dependence properties among users with different physiology, causing the inapplicability of the recognition model on new users. Domain adaptation is the most representative method to reduce the user gap with feature decoupling to acquire motion-related features. However, the existing domain adaptation method shows awful decoupling results when handling complex time-series physiological signals. Therefore, this paper proposes an Iterative Self-Training based Domain Adaptation method (STDA) to supervise the feature decoupling process with the pseudo-label generated by self-training and to explore cross-user sEMG gesture recognition. STDA mainly consists of two parts, discrepancy-based domain adaptation (DDA) and pseudo-label iterative update (PIU). DDA aligns existing users' data and new users' unlabeled data with a Gaussian kernel-based distance constraint. PIU Iteratively continuously updates pseudo-labels to generate more accurate labelled data on new users with category balance. Detailed experiments are performed on publicly available benchmark datasets, including the NinaPro dataset (DB-1 and DB-5) and the CapgMyo dataset (DB-a, DB-b, and DB-c). Experimental results show that the proposed method achieves significant performance improvement compared with existing sEMG gesture recognition and domain adaption methods.
关键词	Surface electromyography gesture recognition cross-user domain adaptation semi-supervised learning
DOI	10.1109/TNSRE.2023.3293334
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2021YFC2501202] ; National Natural Science Foundation of China[61972383] ; National Natural Science Foundation of China[62202455] ; Beijing Municipal Science amp; Technology Commission[Z221100002722009] ; Innovative Research Program of Shandong Academy of Intelligent Computing Technology[SDAICT2191010]
WOS研究方向	Engineering ; Rehabilitation
WOS类目	Engineering, Biomedical ; Rehabilitation
WOS记录号	WOS:001037757900002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：7[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/2XEOYT63/21340
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.Qilu Univ Technol, Shandong Acad Sci, Sch Comp Sci & Technol, Jinan 250353, Peoples R China 2.Shangdong Acad Intelligent Comp Technol, Jinan 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 4.Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Wang, Kang,Chen, Yiqiang,Zhang, Yingwei,et al. Iterative Self-Training Based Domain Adaptation for Cross-User sEMG Gesture Recognition[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2023,31:2974-2987.
APA	Wang, Kang,Chen, Yiqiang,Zhang, Yingwei,Yang, Xiaodong,&Hu, Chunyu.(2023).Iterative Self-Training Based Domain Adaptation for Cross-User sEMG Gesture Recognition.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,31,2974-2987.
MLA	Wang, Kang,et al."Iterative Self-Training Based Domain Adaptation for Cross-User sEMG Gesture Recognition".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 31(2023):2974-2987.