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Predicting physical activity and functional levels through inertial signals and EMD-based features in older adults

2018-09Journal article Restricted access
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dc.contributor.authorGalán-Mercant, Alejandro
dc.contributor.authorMoral-Munoz, Jose A.
dc.contributor.authorOrtiz, Andrés
dc.contributor.authorHerrera-Viedma, Enrique
dc.contributor.authorTomás, Maria Teresa
dc.date.accessioned2018-10-09T14:46:29Z
dc.date.available2018-10-09T14:46:29Z
dc.date.issued2018-09
dc.description.abstractOlder adults are related to a reduction in the 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 the Empirical Mode Decomposition (EMD) method 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 BMI), physical activity, and functional fitness levels from each participant were recorded. Consecutively, the EMD method was 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 m-health systems to identify the physical activity profile of the older adults.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGalán-Mercant A, Moral-Munoz JA, Ortiz A, Herrera-Viedma E, Tomás MT. Predicting physical activity and functional levels through inertial signals and EMD-based features in older adults. In: New Trends in Intelligent Software Methodologies, Tools and Techniques: proceedings of the 17th International Conference SoMeT_18 (Vol. 303). IOS Press; 2018. p. 954-66.pt_PT
dc.identifier.doi10.3233/978-1-61499-900-3-954pt_PT
dc.identifier.isbn978-1-61499-900-3
dc.identifier.urihttp://hdl.handle.net/10400.21/8901
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIOS Presspt_PT
dc.relation.publisherversionhttp://ebooks.iospress.nl/volumearticle/50000pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectPhysical activitypt_PT
dc.subjectFunctional fitnesspt_PT
dc.subjectEmpirical mode decompositionpt_PT
dc.subjectInertial signalpt_PT
dc.subjectClassificationpt_PT
dc.titlePredicting physical activity and functional levels through inertial signals and EMD-based features in older adultspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage966pt_PT
oaire.citation.startPage954pt_PT
oaire.citation.volume303pt_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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