CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression
Li, Q. L.1; Song, Y.2; Hou, Z. G.3
Source PublicationNEURAL PROCESSING LETTERS
2015-06-01
Volume41Issue:3Pages:371-388
SubtypeArticle
AbstractIn this paper, a new technique for predicting human lower limb periodic motions from multi-channel surface ElectroMyoGram (sEMG) was proposed on the basis of least-squares support vector regression (LS-SVR). The sEMG signals were sampled from seven human lower limb muscles. Two channels sEMG were selected and mapped to muscle activation levels for angles estimation based on cross-correlation analysis. To deal with the time delay introduced by low-pass filtering of raw sEMG, a -order dynamic model was derived to represent the dynamic relationship between the joint angles and muscle activation levels. The dynamic model was built by data driven LS-SVR with radial basis function kernel. The inputs of the LS-SVR are muscle activation levels, and the outputs are joint angles of the hip and knee. In experiments, 48 sEMG-angle datasets sampled from six healthy people were utilized to verify the effectiveness of the proposed method. Result shows that the human lower limb joint angles can be well estimated in different motion conditions.
KeywordSemg Ls-svr Motion Estimation Neural Network Support Vector Machine Rehabilitation
WOS HeadingsScience & Technology ; Technology
WOS KeywordNEURAL-NETWORK ; JOINT ; ELECTROMYOGRAPHY
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000354203700006
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8071
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.China Univ Min & Technol, Sch Mech Elect & Informat Engn, Beijing, Peoples R China
2.Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Li, Q. L.,Song, Y.,Hou, Z. G.. Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression[J]. NEURAL PROCESSING LETTERS,2015,41(3):371-388.
APA Li, Q. L.,Song, Y.,&Hou, Z. G..(2015).Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression.NEURAL PROCESSING LETTERS,41(3),371-388.
MLA Li, Q. L.,et al."Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression".NEURAL PROCESSING LETTERS 41.3(2015):371-388.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Q. L.]'s Articles
[Song, Y.]'s Articles
[Hou, Z. G.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Q. L.]'s Articles
[Song, Y.]'s Articles
[Hou, Z. G.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Q. L.]'s Articles
[Song, Y.]'s Articles
[Hou, Z. G.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.