Knowledge Commons of Institute of Automation,CAS
A Multimodal Fusion Model for Estimating Human Hand Force Comparing surface electromyography and ultrasound signals | |
Zou, Yongxiang1,2; Cheng, Long1,2; Li, Zhengwei2 | |
发表期刊 | IEEE ROBOTICS & AUTOMATION MAGAZINE |
ISSN | 1070-9932 |
2022-12-01 | |
卷号 | 29期号:4页码:10-24 |
通讯作者 | Zou, Yongxiang(zouyongxiang2019@ia.ac.cn) |
摘要 | Biomimetic robots have received significant attention in recent years. Among them, the wearable exoskeleton, which imitates the functions of the musculoskeletal system to assist humans, is a typical biomimetic robot. Given that safe human-robot interaction plays a critical role in the successful application of wearable exoskeletons, this work studies the clinical readiness of a multimodal fusion model that estimates hand force based on the surface electromyography (sEMG) and A-mode ultrasound signals of the forearm muscles. The proposed multimodal fusion model affords the biomimetic hand exoskeleton assisting the elderly in completing daily tasks or quantitatively assessing the recovery level of poststroke patients. The suggested fusion model is called Optimization of Latent Representation for the Self-Attention Convolutional Neural Network (OLR-SACNN), which utilizes a common component extraction module (CCEM) and a complementary component retention module (CCRM) to optimize latent representation of the multiple modalities. Then the optimized latent representations are fused with the self-attention mechanism. The experiments conducted on a self-collected multimodal data set verify performance of the proposed OLR-SACNN model. Specifically, compared to solely employing sEMG or A-mode ultrasound signals, the force estimation's normalized mean-square error (NMSE) based on the multiple modalities decreases by 97.7 and 38.92%, respectively. Furthermore, the OLR-SACNN model has been used to estimate the hand force of some poststroke patients and attained the desired performance. |
关键词 | Force Ultrasonic imaging Muscles Robots Feature extraction Estimation Exoskeletons |
DOI | 10.1109/MRA.2022.3177486 |
关键词[WOS] | EXOSKELETON |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62025307] ; National Natural Science Foundation of China[U1913209] ; Beijing Municipal Natural Science Foundation[JQ19020] ; Department of Mathematics and Theories, Peng Cheng Laboratory, China |
项目资助者 | National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Department of Mathematics and Theories, Peng Cheng Laboratory, China |
WOS研究方向 | Automation & Control Systems ; Robotics |
WOS类目 | Automation & Control Systems ; Robotics |
WOS记录号 | WOS:000900084900004 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51106 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Zou, Yongxiang |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Inst Automation, Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zou, Yongxiang,Cheng, Long,Li, Zhengwei. A Multimodal Fusion Model for Estimating Human Hand Force Comparing surface electromyography and ultrasound signals[J]. IEEE ROBOTICS & AUTOMATION MAGAZINE,2022,29(4):10-24. |
APA | Zou, Yongxiang,Cheng, Long,&Li, Zhengwei.(2022).A Multimodal Fusion Model for Estimating Human Hand Force Comparing surface electromyography and ultrasound signals.IEEE ROBOTICS & AUTOMATION MAGAZINE,29(4),10-24. |
MLA | Zou, Yongxiang,et al."A Multimodal Fusion Model for Estimating Human Hand Force Comparing surface electromyography and ultrasound signals".IEEE ROBOTICS & AUTOMATION MAGAZINE 29.4(2022):10-24. |
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