A Multimodal Fusion Model for Estimating Human Hand Force Comparing surface electromyography and ultrasound signals
文献类型：期刊论文
| 作者 | Zou, Yongxiang1,2 ; Cheng, Long1,2; Li, Zhengwei1
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| 刊名 | IEEE ROBOTICS & AUTOMATION MAGAZINE
 |
| 出版日期 | 2022-12-01 |
| 卷号 | 29期号:4页码:10-24 |
| ISSN号 | 1070-9932 |
| 关键词 | Force Ultrasonic imaging Muscles Robots Feature extraction Estimation Exoskeletons |
| DOI | 10.1109/MRA.2022.3177486 |
| 通讯作者 | Zou, Yongxiang(zouyongxiang2019@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | EXOSKELETON |
| 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 |
| WOS研究方向 | Automation & Control Systems ; Robotics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000900084900004 |
| 资助机构 | National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Department of Mathematics and Theories, Peng Cheng Laboratory, China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51105]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Zou, Yongxiang |
| 作者单位 | 1.Inst Automation, Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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