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dc.contributor.authorGalán Mercant, Alejandro
dc.contributor.authorOrtiz-García, Andrés 
dc.contributor.authorHerrera Viedma, Enrique
dc.contributor.authorTomas, Maria Teresa
dc.contributor.authorFernandes, Beatriz
dc.contributor.authorMoral-Muñoz, Jose A.
dc.date.accessioned2023-11-22T07:44:14Z
dc.date.available2023-11-22T07:44:14Z
dc.date.issued2019-12-01
dc.identifier.citationGalán-Mercant, Alejandro & Ortiz, Andrés & Herrera-Viedma, Enrique & Tomás, Maria Teresa & Fernandes, Beatriz & Moral-Munoz, Jose. (2019). Assessing Physical Activity and Functional Fitness Level Using Convolutional Neural Networks. Knowledge-Based Systems. 185. 10.1016/j.knosys.2019.104939.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28103
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.es_ES
dc.description.sponsorshipThis work was partly supported by the MINECO/FEDER under TEC2015-64718-R, PSI2015-65848-R and PGC2018-098813-B-C32 projects; and the Erasmus+ Strategic Partnership for Higher Education Programme (Key Action 203) [Grant number: 2018-1-PL01-KA203-051055]. Furthermore, the mobility grant EST2018-090 was supported by the University of Cádiz . We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRedes neuronales (Informática)es_ES
dc.subjectEjercicio físico - Evaluaciónes_ES
dc.subjectAncianos - Condición físicaes_ES
dc.subject.otherPhysical activityes_ES
dc.subject.otherFunctional fitnesses_ES
dc.subject.otherDeep learninges_ES
dc.subject.otherInertial signales_ES
dc.subject.otherDeep Convolutional Autoencoder/sep Convolutional Networkes_ES
dc.titleAssessing Physical Activity and Functional Fitness Level Using Convolutional Neural Networks.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doi10.1016/j.knosys.2019.104939
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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