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dc.contributor.authorFister, Iztok Jr.
dc.contributor.authorSalcedo-Sanz, Sancho
dc.contributor.authorIglesias Prieto, Andrés 
dc.contributor.authorFister, Dušan
dc.contributor.authorGálvez Tomida, Akemi 
dc.contributor.authorFister, Iztok
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2022-01-14T14:40:53Z
dc.date.available2022-01-14T14:40:53Z
dc.date.issued2021
dc.identifier.issn2076-3417
dc.identifier.otherTIN2017-89275-Res_ES
dc.identifier.otherMCIN/AEI /10.13039/501100011033/FEDERes_ES
dc.identifier.urihttp://hdl.handle.net/10902/23735
dc.description.abstractThe rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines.es_ES
dc.description.sponsorshipAkemi Galvez and Andres Iglesias have received funding from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement no. 778035, and from the project TIN2017-89275-R funded by MCIN/AEI/10.13039/501100011033/FEDER “Una manera de hacer Europa”.es_ES
dc.format.extent14 p.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAttribution 4.0 International. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceApplied Sciences, 2021, 11(23), 11452es_ES
dc.subject.otherArtificial sport traineres_ES
dc.subject.otherDigital twines_ES
dc.subject.otherCognitive modelses_ES
dc.subject.otherComputational intelligencees_ES
dc.titleNew perspectives in the development of the artificial sport traineres_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.3390/app112311452es_ES
dc.rights.accessRightsopenAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/778035/EU/PDE-based geometric modelling, image processing, and shape reconstruction/PDE-GIR/es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-89275-R/ES/SWARM INTELLIGENCE PARA MODELADO Y RECONSTRUCCION DE FORMAS EN GRAFICOS POR COMPUTADOR, IMAGENES MEDICAS Y ROBOTICA/es_ES
dc.identifier.DOI10.3390/app112311452
dc.type.versionpublishedVersiones_ES


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Attribution 4.0 International. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution 4.0 International. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.