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dc.contributor.authorLi, W
dc.contributor.authorLiu, K
dc.contributor.authorSun, Z
dc.contributor.authorLi, C
dc.contributor.authorChai, Y
dc.contributor.authorGu, J
dc.date.accessioned2021-09-02T10:55:56Z
dc.date.available2021-09-02T10:55:56Z
dc.date.issued2022-01
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.other103115
dc.identifier.urihttp://hdl.handle.net/10026.1/17754
dc.description.abstract

In this paper, a novel prediction model is proposed to estimate human continuous motion intention using a fuzzy wavelet neural network (FWNN) and a zeroing neural network (ZNN). During walking, seven channel surface electromyography (sEMG) signals and motion data of hip and knee are collected, and two signals are selected and processed from the seven muscles based on physiological and correlation analysis. Then, FWNN is built as an intention recognition model, with sEMG signals as input and physical hip and knee information as output. Meanwhile, ZNN is exploited into the FWNN model, forming a hybrid model to eliminate the prediction errors of the FWNN model. Finally, comparative numerical simulations are established to indicate the validity of the FWNN–ZNN model with root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) as evaluation indexes. Results show that the proposed FWNN–ZNN model can more accurately estimate human motion intention, which lays the theoretical foundation for the human–robot interaction of rehabilitation robots.

dc.format.extent103115-103115
dc.languageen
dc.language.isoen
dc.publisherElsevier
dc.subjectNeural network
dc.subjectSurface electromyography
dc.subjectContinuous estimation
dc.subjectCorrelation analysis
dc.subjectZeroing neural network
dc.titleA neural network-based model for lower limb continuous estimation against the disturbance of uncertainty
dc.typejournal-article
dc.typeArticle
plymouth.volume71
plymouth.publisher-urlhttp://dx.doi.org/10.1016/j.bspc.2021.103115
plymouth.publication-statusPublished
plymouth.journalBiomedical Signal Processing and Control
dc.identifier.doi10.1016/j.bspc.2021.103115
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.dateAccepted2021-08-23
dc.rights.embargodate2022-9-2
dc.identifier.eissn1746-8108
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.bspc.2021.103115
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2022-01
rioxxterms.typeJournal Article/Review


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