Show simple item record

dc.contributor.authorZhang, X
dc.contributor.authorLiu, K
dc.contributor.authorLi, Chunxu
dc.contributor.authorYi, J
dc.contributor.authorDuan, X
dc.contributor.authorSun, Z
dc.date.accessioned2021-06-30T11:28:08Z
dc.date.available2021-06-30T11:28:08Z
dc.date.issued2021-05-14
dc.identifier.isbn9781665424233
dc.identifier.issn2767-9861
dc.identifier.urihttp://hdl.handle.net/10026.1/17288
dc.descriptionNo embargo required.
dc.description.abstract

In this paper, a method fusing least squares support vector machine (LS-SVM) with Gaussian kernel function and zeroing neural network (ZNN) is proposed to forecast the continuous motion of lower limb. The surface electromyography (sEMG) signal contains human behavior information and directly reflects the movement intention. In the experiment, the sEMG signal of the lower limbs when the tester is doing leg extension exercise is collected and the real knee joint angle is recorded at the same time. Then the raw sEMG is subjected to a series of preprocessing, and the corresponding muscle activation is gained by calculation. The muscle activation is used as input to the LS-SVM model, and the output to the model is the knee joint angle. LS-SVM transforms the original problem into solving linear equations. When the amount of data is relatively large, the traditional solution method is very time-consuming, and the proposed ZNN method is able to solve the problem, thus speeding up the convergence speed and greatly reducing the learning time. Finally, the back propagation neural network (BPNN) model is utilized to form a comparative experiment. The numerical results indicate that a more stable and better performance is reflected in the raised method, which provides a valuable reference for the research of joint continuous estimation.

dc.format.extent139-144
dc.language.isoen
dc.publisherIEEE
dc.titleLS-SVM Combined with ZNN for Predicting the Continuous Motion Joint Angle of Lower Limb
dc.typeconference
dc.typeConference Proceeding
plymouth.date-start2021-05-14
plymouth.date-finish2021-05-16
plymouth.volume00
plymouth.publisher-urlhttps://ieeexplore.ieee.org/document/9455639
plymouth.conference-name2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)
plymouth.publication-statusPublished
plymouth.journal2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)
dc.identifier.doi10.1109/ddcls52934.2021.9455639
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2021-06-25
dc.rights.embargodate2021-7-2
dc.identifier.eissn2767-9861
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1109/ddcls52934.2021.9455639
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-05-14
rioxxterms.typeConference Paper/Proceeding/Abstract


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
Atmire NV