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Assessing physical activity and functional fitness level using convolutional neural networks

2019-08Journal article Restricted access
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dc.contributor.authorGalán-Mercant, Alejandro
dc.contributor.authorOrtiz, Andrés
dc.contributor.authorHerrera-Viedma, Enrique
dc.contributor.authorTomás, Maria Teresa
dc.contributor.authorFernandes, Beatriz
dc.contributor.authorMoral-Munoz, Jose A.
dc.date.accessioned2019-08-27T11:17:57Z
dc.date.available2019-08-27T11:17:57Z
dc.date.issued2019-08
dc.descriptionMINECO/FEDER under TEC2015-64718-R, PSI2015-65848-R and PGC2018-098813-B-C32 projects. Erasmus+ Strategic Partnership for Higher Education Programme (Key Action 203) [Grant number: 2018-1-PL01-EKA203-051055].pt_PT
dc.description.abstractOlder adults are related to a reduction in physical functionality, as a result of a musculoskeletal system degeneration. In that way, physical exercise has been stated as a suitable intervention to prevent such health problems. Therefore, an adequate assessment of the physical activity and functional fitness levels is needed to plan the individualized intervention. A broad test used to assess the functional fitness level is the 6-minutes walk test (6MWT). It has been previously measured using accelerometer sensors. In views of this background, the main aim of the present study is to use deep learning to extract automatically and to predict the physical activity and functional fitness levels of the older adults through the acceleration signals recorded by a smartphone during the 6MWT. A total of 17 participants were recruited. Anthropometric measurements (weight, height, and body mass index), physical activity, and functional fitness levels from each participant were recorded. Consecutively, two deep learning-based methods were applied to determine the prediction. According to the results, the proposed method can predict physical activity and functional fitness levels with high accuracy, even using only one cycle. Thus, the approach described in the present work could be implemented in future mobile health systems to identify the physical activity profile of older adults.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGalán-Mercant A, Ortiz A, Herrera-Viedma E, Tomás MT, Fernandes B, Moral-Munoz JA. Assessing physical activity and functional fitness level using convolutional neural networks. Knowl Based Syst. 2019;185:104939.pt_PT
dc.identifier.doi10.1016/j.knosys.2019.104939pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/10425
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationMINECO/FEDER_TEC2015-64718-R projectpt_PT
dc.relationMINECO/FEDER_PSI2015-65848-R projectpt_PT
dc.relationMINECO/FEDER_PGC2018-098813-B-C32 projectpt_PT
dc.relationErasmus+ Strategic Partnership for Higher Education Programme (Key Action 203)_Grant number: 2018-1-PL01-EKA203-051055pt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0950705119303806?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectPhysiotherapypt_PT
dc.subjectRehabilitationpt_PT
dc.subjectPhysical activitypt_PT
dc.subjectFunctional fitnesspt_PT
dc.subjectDeep learningpt_PT
dc.subjectInertial signalpt_PT
dc.subjectDeep convolutional autoencoder/sep convolutional networkpt_PT
dc.subjectMINECO/FEDER_TEC2015-64718-R projectpt_PT
dc.subjectMINECO/FEDER_PSI2015-65848-R projectpt_PT
dc.subjectMINECO/FEDER_PGC2018-098813-B-C32 projectpt_PT
dc.subjectGrant number: 2018-1-PL01-EKA203-051055pt_PT
dc.titleAssessing physical activity and functional fitness level using convolutional neural networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage104939pt_PT
oaire.citation.startPage104939pt_PT
oaire.citation.titleKnowledge-Based Systemspt_PT
oaire.citation.volume185pt_PT
person.familyNameTomás
person.givenNameMaria Teresa
person.identifier438585
person.identifier.ciencia-id3010-19D6-C7A5
person.identifier.orcid0000-0003-0491-8903
person.identifier.ridN-1940-2013
person.identifier.scopus-author-id36700434200
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication64ad74a4-4cd4-426e-a1ee-2ec846fdc6dd
relation.isAuthorOfPublication.latestForDiscovery64ad74a4-4cd4-426e-a1ee-2ec846fdc6dd

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