Show simple item record

dc.contributor.authorRabbi, Mohammad Fazle
dc.contributor.authorPizzolato, Claudio
dc.contributor.authorLloyd, David G
dc.contributor.authorCarty, Chris P
dc.contributor.authorDevaprakash, Daniel
dc.contributor.authorDiamond, Laura E
dc.date.accessioned2020-05-25T23:09:08Z
dc.date.available2020-05-25T23:09:08Z
dc.date.issued2020
dc.identifier.issn2045-2322
dc.identifier.doi10.1038/s41598-020-65257-w
dc.identifier.urihttp://hdl.handle.net/10072/394132
dc.description.abstractMuscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate method for muscle synergy extraction is currently unknown. In this study, we compared four muscle synergy extraction methods: non-negative matrix factorisation, principal component analysis, independent component analysis, and factor analysis. Probability distribution of muscle activation patterns were compared with the probability distribution of synergy excitation primitives obtained from the four factorisation methods. Muscle synergies extracted using non-negative matrix factorisation best matched the probability distribution of muscle activation patterns across different walking and running speeds. Non-negative matrix factorisation also best tracked changes in muscle activation patterns compared to the other factorisation methods. Our results suggest that non-negative matrix factorisation is the best factorisation method for identifying muscle synergies in dynamic tasks with different levels of muscle contraction.
dc.description.peerreviewedYes
dc.description.sponsorshipArthritis Australia
dc.description.sponsorshipGriffith University
dc.description.sponsorshipGriffith University
dc.description.sponsorshipMotor Accident Insurance Commission
dc.description.sponsorshipThe University of Melbourne ARC
dc.description.sponsorshipPerpetual Trustees
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofpagefrom8266:1
dc.relation.ispartofpageto8266:11
dc.relation.ispartofissue1
dc.relation.ispartofjournalScientific Reports
dc.relation.ispartofvolume10
dc.relation.urihttp://purl.org/au-research/grants/NHMRC/LP150100905
dc.relation.urihttp://purl.org/au-research/grants/NHMRC/APP1143660
dc.relation.urihttp://purl.org/au-research/grants/NHMRC/IC180100024
dc.relation.urihttp://purl.org/au-research/grants/ARC/LP150100905
dc.relation.urihttp://purl.org/au-research/grants/ARC/APP1143660
dc.relation.urihttp://purl.org/au-research/grants/ARC/IC180100024
dc.relation.grantIDLP150100905
dc.relation.grantIDAPP1143660
dc.relation.grantIDIC180100024
dc.relation.fundersNHMRC
dc.relation.fundersARC
dc.subject.fieldofresearchBiochemistry and Cell Biology
dc.subject.fieldofresearchOther Physical Sciences
dc.subject.fieldofresearchcode0601
dc.subject.fieldofresearchcode0299
dc.titleNon-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationRabbi, MF; Pizzolato, C; Lloyd, DG; Carty, CP; Devaprakash, D; Diamond, LE, Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running, Scientific Reports, 2020, 10 (1), pp. 8266:1-8266:11
dcterms.dateAccepted2020-04-28
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-05-25T06:51:20Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
gro.hasfulltextFull Text
gro.griffith.authorDevaprakash, Daniel
gro.griffith.authorPizzolato, Claudio
gro.griffith.authorDiamond, Laura
gro.griffith.authorRabbi, Fazle
gro.griffith.authorLloyd, David
gro.griffith.authorCarty, Chris P.


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record