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dc.contributor.authorPauk, Jolanta
dc.contributor.authorDaunoravičienė, Kristina
dc.contributor.authorŽižienė, Jurgita
dc.contributor.authorMinta-Bielecka, Katarzyna
dc.contributor.authorDzieciol-Anikiej, Zofia
dc.date.accessioned2023-09-18T16:36:17Z
dc.date.available2023-09-18T16:36:17Z
dc.date.issued2023
dc.identifier.issn1746-8094
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/115360
dc.description.abstractIn recent years, there has been major interest in recognising electromyography (EMG) patterns. This work proposes a new method based on a biclustering algorithm which can group strides showing homogeneous EMG activation intervals. The surface EMG signals of biceps femoris, rectus femoris, semitendinosus, lateral gastrocnemius, and medial gastrocnemius muscles of 17 healthy children aged between 4 and 11 years old were obtained using a Trigno EMG wireless system. The data set was tested for different values of parameter α (the threshold describing when the multiple node deletion step is used) and δ (the threshold that limits the value of the mean square residue). The highest number of coincidences of muscle activation was observed in 6 to 7-year-old subjects. This was not affected by their anthropometrics or gender. The obtained biclusters reflect actual differences between the subjects’ gait parameters, namely stride length, stride time, and walking speed. These results can be used to develop strategies for finding homogeneous groups of patients.eng
dc.formatPDF
dc.format.extentp. 1-10
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyINSPEC
dc.relation.isreferencedbyGale's Academic OneFile
dc.source.urihttps://www.sciencedirect.com/science/article/pii/S1746809423001647?via%3Dihub
dc.titleClassification of muscle activity patterns in healthy children using biclustering algorithm
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references46
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionBialystok University of Technology
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionMedical University of Bialystok
dc.contributor.facultyMechanikos fakultetas / Faculty of Mechanics
dc.subject.researchfieldT 009 - Mechanikos inžinerija / Mechanical enginering
dc.subject.studydirectionE02 - Bioinžinerija / Bioengineering
dc.subject.studydirectionE06 - Mechanikos inžinerija / Mechanical engineering
dc.subject.vgtuprioritizedfieldsMC0404 - Bionika ir biomedicinos inžinerinės sistemos / Bionics and Biomedical Engineering Systems
dc.subject.ltspecializationsL105 - Sveikatos technologijos ir biotechnologijos / Health technologies and biotechnologies
dc.subject.enbiclustering algorithm
dc.subject.enEMG pattern
dc.subject.enchildren gait
dc.subject.enhomogenous group
dcterms.sourcetitleBiomedical signal processing and control
dc.description.volumevol. 84
dc.publisher.nameElsevier Ltd.
dc.publisher.cityOxford
dc.identifier.doi000953374300001
dc.identifier.doi10.1016/j.bspc.2023.104731
dc.identifier.elaba157974615


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