Näytä suppeat kuvailutiedot

dc.contributor.authorAshfaq, Atiqa
dc.contributor.authorCronin, Neil
dc.contributor.authorMüller, Philipp
dc.date.accessioned2022-02-02T07:23:57Z
dc.date.available2022-02-02T07:23:57Z
dc.date.issued2022
dc.identifier.citationAshfaq, A., Cronin, N., & Müller, P. (2022). Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review. <i>Informatics in Medicine Unlocked</i>, <i>28</i>, Article 100863. <a href="https://doi.org/10.1016/j.imu.2022.100863" target="_blank">https://doi.org/10.1016/j.imu.2022.100863</a>
dc.identifier.otherCONVID_104091151
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/79610
dc.description.abstractMaximal oxygen uptake ( max) is the maximum amount of oxygen attainable by a person during exercise. max is used in different domains including sports and medical sciences and is usually measured during an incremental treadmill or cycle ergometer test. The drawback of directly measuring max using the maximal test is that it is expensive and requires a fixed and controlled protocol. During the last decade, various machine learning models have been developed for max prediction and numerous studies have attempted to predict max using data from submaximal and non-exercise tests. This article gives an overview of the machine learning models developed over the past five years (2016–2021) for the prediction of max. Multiple linear regression, support vector machine, artificial neural network and multilayer perceptron are some of the techniques that have been used to build predictive models using different combinations of predictor variables. Model performance is generally assessed using correlation coefficient (R-value), standard error of estimate (SEE) and root mean squared error (RMSE), computed between ground truth and predicted values. The findings of this review indicate that models using ANN typically outperform other machine learning techniques. Moreover, the predictor variables used to build the model have a large influence on the model's predictive performance.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesInformatics in Medicine Unlocked
dc.rightsCC BY 4.0
dc.subject.otherMaximal oxygen uptake (VO2 max)
dc.subject.otherGraded exercise tests
dc.subject.otherArtificial neural network
dc.subject.otherPrediction models
dc.subject.otherError metrics
dc.titleRecent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202202021372
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.contributor.oppiaineBiomekaniikkafi
dc.contributor.oppiaineBiomechanicsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_dcae04bc
dc.description.reviewstatuspeerReviewed
dc.relation.issn2352-9148
dc.relation.volume28
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 The Authors. Published by Elsevier Ltd.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber323473
dc.subject.ysokoneoppiminen
dc.subject.ysofyysinen kunto
dc.subject.ysoennusteet
dc.subject.ysoneuroverkot
dc.subject.ysomaksimaalinen hapenotto
dc.subject.ysokuntotestit
dc.subject.ysomittaustekniikka
dc.subject.ysomallintaminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p7384
jyx.subject.urihttp://www.yso.fi/onto/yso/p3297
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p25454
jyx.subject.urihttp://www.yso.fi/onto/yso/p17246
jyx.subject.urihttp://www.yso.fi/onto/yso/p5635
jyx.subject.urihttp://www.yso.fi/onto/yso/p3533
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.imu.2022.100863
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis work was supported in part by the Academy of Finland, grants 323472 and 323473 (under consortium “GaitMaven: Machine learning for gait analysis and performance prediction”).
dc.type.okmA2


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