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dc.contributor.authorBisele, Maria
dc.contributor.authorBencsik, Martin
dc.contributor.authorLewis, Martin
dc.contributor.authorBarnett, Cleveland
dc.date.accessioned2019-11-04T09:22:08Z
dc.date.available2019-11-04T09:22:08Z
dc.date.issued2017-09-08
dc.identifier.citationBisele, M., Bencsik, M., Lewis, M.G. and Barnett, C.T., (2017). 'Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses'. PloS one, 12(9), pp, 1-19. DOI: 10.1371/journal.pone.0183990en_US
dc.identifier.issn1932-6203
dc.identifier.doi10.1371/journal.pone.0183990
dc.identifier.urihttp://hdl.handle.net/10545/624287
dc.description.abstractAssessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg-1) and joint powers (W.kg-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors’ knowledge, this is the first study to optimise the development of a machine learning algorithm.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.urlhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183990en_US
dc.subjectBiomechanics, Machine Learning, Sporten_US
dc.titleOptimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analysesen_US
dc.typeArticleen_US
dc.contributor.departmentNottingham Trent Universityen_US
dc.identifier.journalPLOS Oneen_US
dc.source.volume12
dc.source.issue9
dc.source.beginpagee0183990
dcterms.dateAccepted2017-08-15
refterms.dateFOA2017-09-08T00:00:00Z
dc.author.detail786762en_US


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