A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model

doi:10.3389/fbioe.2022.970859

	A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model
	Lu, Wei 2,3; Gao, Lifu 2,3; Cao, Huibin 2,3; Li, Zebin 1,2,3; Wang, Daqing 2
刊名	FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
	2022-09-07
卷号	10
关键词	surface electromyography kernel principal component analysis deep residual shrinkage network mean impact value interaction force prediction
ISSN号	2296-4185
DOI	10.3389/fbioe.2022.970859
通讯作者	Cao, Huibin(hbcao@iim.ac.cn) ; Li, Zebin(robotzebinli@foxmail.com)
英文摘要	Rapid and accurate prediction of interaction force is an effective way to enhance the compliant control performance. However, whether individual muscles or a combination of muscles is more suitable for interaction force prediction under different contraction tasks is of great importance in the compliant control of the wearable assisted robot. In this article, a novel algorithm that is based on sEMG and KPCA-DRSN is proposed to explore the relationship between interaction force prediction and sEMG signals. Furthermore, the contribution of each muscle to the interaction force is assessed based on the predicted results. First of all, the experimental platform for obtaining the sEMG is described. Then, the raw sEMG signal of different muscles is collected from the upper arm during different contractions. Meanwhile, the output force is collected by the force sensor. The Kernel Principal Component Analysis (KPCA) method is adopted to remove the invalid components of the raw sEMG signal. After that, the processed sequence is fed into the Deep Residual Shrinkage Network (DRSN) to predict the interaction force. Finally, based on the prediction results, the contribution of each sEMG signal from different muscles to the interaction force is evaluated by the mean impact value (MIV) indicator. The experimental results demonstrate that our methods can automatically extract the valid features of sEMG signal and provided fast and efficient prediction. In addition, the single muscle with the largest MIV index could predict the interaction force faster and more accurately than the muscle combination in different contraction tasks. The finding of our research provides a solid evidence base for the compliant control of the wearable robot.
资助项目	Key Research and Development Project of Anhui Province[2022a05020035] ; Major Science and Technology Project of Anhui Province[202103a05020022] ; National Natural Science Foundation of China[92067205] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA22040303] ; Key scientific research projects of Anhui Province higher education[KJ2020A0630] ; HFIPS Director's Fund[YZJJ2021QN25]
WOS研究方向	Biotechnology & Applied Microbiology ; Science & Technology - Other Topics
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000886528300001
资助机构	Key Research and Development Project of Anhui Province ; Major Science and Technology Project of Anhui Province ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key scientific research projects of Anhui Province higher education ; HFIPS Director's Fund
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/130409]
专题	中国科学院合肥物质科学研究院
通讯作者	Cao, Huibin; Li, Zebin
作者单位	1.West Anhui Univ, Sch Elect & Photoelect Engn, Luan, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China 3.USTC, Sci Isl Branch, Grad Sch, Hefei, Peoples R China
推荐引用方式 GB/T 7714	Lu, Wei,Gao, Lifu,Cao, Huibin,et al. A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model[J]. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY,2022,10.
APA	Lu, Wei,Gao, Lifu,Cao, Huibin,Li, Zebin,&Wang, Daqing.(2022).A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model.FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY,10.
MLA	Lu, Wei,et al."A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model".FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 10(2022).