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dc.contributor.authorNieto Reyes, Alicia 
dc.contributor.authorDuque Medina, Rafael 
dc.contributor.authorMontaña Arnaiz, José Luis 
dc.contributor.authorLage, Carmen
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.description.abstractFunctional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c?s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of 83% indicates the potential of the proposed methodologyes_ES
dc.description.sponsorshipThis work was partially supported by project PAC::LFO of the Spanish Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia under grant MTM2014-55262-P, and by the Spanish Ministerio de Economía y Competitividad under grant MTM2014-56235-C2-2-P. We gratefully acknowledge the “Asociación de Familiares de Enfermos de Alzheimer en Cantabria” and Pablo Cobo García for their participation in the various studies.es_ES
dc.format.extent18 p.es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.sourceSensors 2017, 17, 1679es_ES
dc.titleClassification of Alzheimer's patients through ubiquitous computinges_ES

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Atribución 4.0 InternacionalExcept where otherwise noted, this item's license is described as Atribución 4.0 Internacional