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dc.contributor.authorNiemelä, Marko
dc.contributor.authorKulmala, Juha-Pekka
dc.contributor.authorKauppi, Jukka-Pekka
dc.contributor.authorKosonen, Jukka
dc.contributor.authorÄyrämö, Sami
dc.date.accessioned2017-08-31T05:20:58Z
dc.date.available2018-05-15T21:45:06Z
dc.date.issued2017
dc.identifier.citationNiemelä, M., Kulmala, J.-P., Kauppi, J.-P., Kosonen, J., & Äyrämö, S. (2017). Prediction of active peak force using a multilayer perceptron. <i>Sports Engineering</i>, <i>20</i>(3), 213-219. <a href="https://doi.org/10.1007/s12283-017-0236-z" target="_blank">https://doi.org/10.1007/s12283-017-0236-z</a>
dc.identifier.otherCONVID_27009564
dc.identifier.otherTUTKAID_73814
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55223
dc.description.abstractBoth kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas GRF during the support phase of running is measured by force platforms. To analyze both kinematics and kinetics of a runner over several subsequent contacts, an instrumented treadmill or alternatively several force platforms installed over a regulated space are available options, but they are highly immovable, expensive, and sometimes even impractical options. Naturally, it would be highly useful to predict GRFs using a motion capture system only and this way reduce costs and complexity of the analysis. In this study, the machine learning model for vertical GRF magnitude prediction based on running motion information of 128 healthy adults is proposed. The predicted outputs of a multilayer perceptron model were compared with the actual force platform measurements. The results were evaluated with Pearson’s correlation coefficient through a tenfold cross validation. The mean standard error of the estimate was 0.107 body weights showing that our method is sufficiently accurate to identify abnormalities in running technique among recreational runners.
dc.language.isoeng
dc.publisherSpringer London
dc.relation.ispartofseriesSports Engineering
dc.subject.otherground reaction force
dc.subject.othermultilayer perceptron
dc.subject.othergait analysis
dc.subject.otherrunning motion capture system
dc.subject.otherforce platform
dc.titlePrediction of active peak force using a multilayer perceptron
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201708253573
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineBiomekaniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineBiomechanicsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2017-08-25T15:15:05Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange213-219
dc.relation.issn1369-7072
dc.relation.numberinseries3
dc.relation.volume20
dc.type.versionacceptedVersion
dc.rights.copyright© International Sports Engineering Association 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.subject.ysojuoksu
dc.subject.ysobiomekaniikka
dc.subject.ysoliikeoppi
dc.subject.ysovoimantuotto (fysiologia)
dc.subject.ysoliikeanalyysi
dc.subject.ysoliikkeenkaappaus
jyx.subject.urihttp://www.yso.fi/onto/yso/p9087
jyx.subject.urihttp://www.yso.fi/onto/yso/p20292
jyx.subject.urihttp://www.yso.fi/onto/yso/p16028
jyx.subject.urihttp://www.yso.fi/onto/yso/p25323
jyx.subject.urihttp://www.yso.fi/onto/yso/p24952
jyx.subject.urihttp://www.yso.fi/onto/yso/p27199
dc.relation.doi10.1007/s12283-017-0236-z


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