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Multi-modal fusion for robust hand gesture recognition based on heterogeneous networks

文献类型：期刊论文

作者Zou, Yongxiang1,2; Cheng, Long1,2; Han, Lijun1,2; Li, Zhengwei2
刊名SCIENCE CHINA-TECHNOLOGICAL SCIENCES
出版日期2023-10-08
页码12
ISSN号1674-7321
关键词leap motion sEMG multi-modal graph neural network hand gesture recognition
DOI10.1007/s11431-022-2345-2
通讯作者Cheng, Long(long.cheng@ia.ac.cn)
英文摘要Hand gesture recognition has become a vital subject in the fields of human-computer interaction and rehabilitation assessment. This paper presents a multi-modal fusion for hand gesture recognition (MFHG) model, which uses two heterogeneous networks to extract and fuse the features of the vision-based motion signals and the surface electromyography (sEMG) signals, respectively. To extract the features of the vision-based motion signals, a graph neural network, named the cumulation graph attention (CGAT) model, is first proposed to characterize the prior knowledge of motion coupling between finger joints. The CGAT model uses the cumulation mechanism to combine the early and late extracted features to improve motion-based hand gesture recognition. For the sEMG signals, a time-frequency convolutional neural network model, named TF-CNN, is proposed to extract both the signals' time-domain and frequency-domain information. To improve the performance of hand gesture recognition, the deep features from multiple modes are merged with an average layer, and then the regularization items containing center loss and the mutual information loss are employed to enhance the robustness of this multi-modal system. Finally, a data set containing the multi-modal signals from seven subjects on different days is built to verify the performance of the multi-modal model. The experimental results indicate that the MFHG can reach 99.96% and 92.46% accuracy on hand gesture recognition in the cases of within-session and cross-day, respectively.
资助项目National Key Research& Development Program of China[2022YFB4703204] ; Project for Young Scientists in Basic Research of Chinese Academy of Sciences[YSBR-034]
WOS研究方向Engineering ; Materials Science
语种英语
出版者SCIENCE PRESS
WOS记录号WOS:001081161800001
资助机构National Key Research& Development Program of China ; Project for Young Scientists in Basic Research of Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/53053]  
专题多模态人工智能系统全国重点实验室
通讯作者Cheng, Long
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zou, Yongxiang,Cheng, Long,Han, Lijun,et al. Multi-modal fusion for robust hand gesture recognition based on heterogeneous networks[J]. SCIENCE CHINA-TECHNOLOGICAL SCIENCES,2023:12.
APA Zou, Yongxiang,Cheng, Long,Han, Lijun,&Li, Zhengwei.(2023).Multi-modal fusion for robust hand gesture recognition based on heterogeneous networks.SCIENCE CHINA-TECHNOLOGICAL SCIENCES,12.
MLA Zou, Yongxiang,et al."Multi-modal fusion for robust hand gesture recognition based on heterogeneous networks".SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2023):12.

入库方式： OAI收割

来源：自动化研究所

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