Decoding Electromyographic Signal With Multiple Labels for Hand Gesture Recognition

doi:10.1109/LSP.2023.3264417

	Decoding Electromyographic Signal With Multiple Labels for Hand Gesture Recognition
	Zou, Yongxiang1,2,3 ; Cheng, Long1,2,3 ; Han, Lijun1,2,3 ; Li, Zhengwei2,3 ; Song, Luping 4
发表期刊	IEEE SIGNAL PROCESSING LETTERS
ISSN	1070-9908
	2023
卷号	30 页码:483-487
通讯作者	Song, Luping(songluping882002@aliyun.com)
摘要	Surface electromyography (sEMG) is a significant interaction signal in the fields of human-computer interaction and rehabilitation assessment, as it can be used for hand gesture recognition. This letter proposes a novel MLHG model to improve the robustness of sEMG-based hand gesture recognition. The model utilizes multiple labels to decode the sEMG signals from two different perspectives. In the first view, the sEMG signals are transformed into motion signals using the proposed FES-MSCNN (Feature Extraction of sEMG with Multiple Sub-CNN modules). Furthermore, a discriminator FEM-SAGE (Feature Extraction of Motion with graph SAmple and aggreGatE model) is employed to judge the authenticity of the generated motion data. The deep features of the motion signals are extracted using the FEM-SAGE model. In the second view, the deep features of the sEMG signals are extracted using the FES-MSCNN model. The extracted features of the sEMG signals and the generated motion signals are then fused for hand gesture recognition. To evaluate the performance of the proposed model, a dataset containing sEMG signals and multiple labels from 12 subjects has been collected. The experimental results indicate that the MLHG model achieves an accuracy of 99.26% for within-session hand gesture recognition, 78.47% for cross-time, and 53.52% for cross-subject. These results represent a significant improvement compared to using only the gesture labels, with accuracy improvements of 1.91%, 5.35%, and 5.25% in the within-session, cross-time and cross-subject cases, respectively.
关键词	Feature extraction Gesture recognition Decoding Aggregates Muscles Hospitals Graph neural networks Electromyogram decoding graph neural network hand gesture recognition multiple labels
DOI	10.1109/LSP.2023.3264417
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2022YFB4703204] ; CAS Project for Young Scientists in Basic Research[YSBR-034]
项目资助者	National Key Research and Development Program of China ; CAS Project for Young Scientists in Basic Research
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000982369900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53282
专题	多模态人工智能系统全国重点实验室
通讯作者	Song, Luping
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Huazhong Univ Sci & Technol Union Shenzhen, Shenzhen Peoples Hosp 6, Nanshan Hosp, Shenzhen 518172, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zou, Yongxiang,Cheng, Long,Han, Lijun,et al. Decoding Electromyographic Signal With Multiple Labels for Hand Gesture Recognition[J]. IEEE SIGNAL PROCESSING LETTERS,2023,30:483-487.
APA	Zou, Yongxiang,Cheng, Long,Han, Lijun,Li, Zhengwei,&Song, Luping.(2023).Decoding Electromyographic Signal With Multiple Labels for Hand Gesture Recognition.IEEE SIGNAL PROCESSING LETTERS,30,483-487.
MLA	Zou, Yongxiang,et al."Decoding Electromyographic Signal With Multiple Labels for Hand Gesture Recognition".IEEE SIGNAL PROCESSING LETTERS 30(2023):483-487.