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dc.contributor.authorAutorGonzález-Olguín, Arturo
dc.contributor.authorAutorRamos Rodríguez, Diego
dc.contributor.authorAutorHigueras Córdoba, Francisco
dc.contributor.authorAutorMartínez Rebolledo, Luis
dc.contributor.authorAutorTaramasco, Carla
dc.contributor.authorAutorRobles Cruz, Diego
dc.date.accessionedFecha ingreso2024-09-03T19:21:11Z
dc.date.availableFecha disponible2024-09-03T19:21:11Z
dc.date.issuedFecha publicación2022
dc.identifier.citationReferencia BibliográficaInternational Journal of Environmental Research and Public Health, 19(19), 18 p.
dc.identifier.issnISSN1661-7827
dc.identifier.uriURLhttp://repositorio.udla.cl/xmlui/handle/udla/1608
dc.identifier.uriURLhttps://www.mdpi.com/journal/ijerph
dc.description.abstractResumenBackground: The preoccupation related to the fall, also called fear of falling (FOF) by some authors is of interest in the fields of geriatrics and gerontology because it is related to the risk of falling and subsequent morbidity of falling. This study seeks to classify the acceleration patterns of the center of mass during walking in subjects with mild and moderate knee osteoarthritis (KOA) for three levels of FOF (mild, moderate, and high). (2) Method: Center-of-mass acceleration patterns were recorded in all three planes of motion for a 30-meter walk test. A convolutional neural network (CNN) was implemented for the classification of acceleration signals based on the different levels of FOF (mild, moderate, and high) for two KOA conditions (mild and moderate). (3) Results: For the three levels of FOF to fall and regardless of the degree of KOA, a precision of 0.71 was obtained. For the classification considering the three levels of FOF and only for the mild KOA condition, a precision of 0.72 was obtained. For the classification considering the three levels of FOF and only the moderate KOA condition, a precision of 0.81 was obtained, the same as in the previous case, and finally for the classification for two levels of FOF, a high vs. moderate precision of 0.78 was obtained. For high vs. low, a precision of 0.77 was obtained, and for the moderate vs. low, a precision of 0.8 was obtained. Finally, when considering both KOA conditions, a 0.74 rating was obtained. (4) Conclusions: The classification model based on deep learning (CNN) allows for the adequate discrimination of the acceleration patterns of the moderate class above the low or high FOF.
dc.format.extentdc.format.extent18 páginas
dc.format.extentdc.format.extent814.8Kb
dc.format.mimetypedc.format.mimetypePDF
dc.language.isoLenguaje ISOeng
dc.publisherEditorMDPI
dc.rightsDerechosCreative Commons Attribution License (CC BY)
dc.sourceFuentesInternational Journal of Environmental Research and Public Health
dc.subjectPalabras ClavesFall
dc.subject.lcshdc.subject.lcshMarcha
dc.subject.lcshdc.subject.lcshRodilla
dc.subject.lcshdc.subject.lcshOsteoartritis
dc.subject.meshdc.subject.meshAceleración
dc.subject.meshdc.subject.meshDeep learning
dc.titleTítuloClassification of center of mass acceleration patterns in older people with knee osteoarthritis and fear of falling
dc.typeTipo de DocumentoArtículo
dc.udla.catalogadordc.udla.catalogadorCBM
dc.udla.indexdc.udla.indexWoS
dc.udla.indexdc.udla.indexScopus
dc.udla.indexdc.udla.indexBiomedical Reference Collection: Corporate Edition
dc.udla.indexdc.udla.indexCAB Abstracts
dc.udla.indexdc.udla.indexEMBASE
dc.udla.indexdc.udla.indexHealth Research Premium Collection
dc.udla.indexdc.udla.indexMEDLINE
dc.identifier.doidc.identifier.doi10.3390/ijerph191912890
dc.facultaddc.facultadFacultad de Salud y Ciencias Sociales


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