Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/127390
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dc.contributor.authorD’Alessandro, Tizianaen_US
dc.contributor.authorCarmona Duarte, María Cristinaen_US
dc.contributor.authorDe Stefano, Claudioen_US
dc.contributor.authorDíaz Cabrera, Moisésen_US
dc.contributor.authorFerrer Ballester, Miguel Ángelen_US
dc.contributor.authorFontanella, Francescoen_US
dc.date.accessioned2023-10-25T14:53:35Z-
dc.date.available2023-10-25T14:53:35Z-
dc.date.issued2023en_US
dc.identifier.isbn978-3-031-45460-8en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10553/127390-
dc.description.abstractAlzheimer’s disease is one of the most incisive illnesses among the neurodegenerative ones, and it causes a progressive decline in cognitive abilities that, in the worst cases, becomes severe enough to interfere with daily life. Currently, there is no cure, so an early diagnosis is strongly needed to try and slow its progression through medical treatments. Handwriting analysis is considered a potential tool for detecting and understanding certain neurological conditions, including Alzheimer’s disease. While handwriting analysis alone cannot provide a definitive diagnosis of Alzheimer’s, it may offer some insights and be used for a comprehensive assessment. The Sigma-lognormal model is conceived for movement analysis and can also be applied to handwriting. This model returns a set of lognormal parameters as output, which forms the basis for the computation of novel and significant features. This paper presents a machine learning approach applied to handwriting features extracted through the sigma-lognormal model. The aim is to develop a support system to help doctors in the diagnosis and study of Alzheimer, evaluate the effectiveness of the extracted features and finally study the relation among them.en_US
dc.languageengen_US
dc.publisherSpringeren_US
dc.relationModelo Computacional Del Aprendizajey la Degeneración Del Movimiento Humano Para Su Aplicación en Diagnóstico Clínicoen_US
dc.source21st Conference of the International Graphonomics Society (IGS2023). Lecture Notes in Computer Science 14285, p. 103-121en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject3314 Tecnología médicaen_US
dc.titleA Machine Learning Approach to Analyze the Effects of Alzheimer’s Disease on Handwriting Through Lognormal Featuresen_US
dc.typeinfo:eu-repo/semantics/conferenceobjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference21st Conference of the International Graphonomics Society (IGS2023)en_US
dc.identifier.doi10.1007/978-3-031-45461-5_8en_US
dc.description.lastpage121en_US
dc.description.firstpage103en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.description.numberofpages19en_US
dc.utils.revisionen_US
dc.date.coverdateOctubre 2023en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INGen_US
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Física-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4441-6652-
crisitem.author.orcid0000-0003-3878-3867-
crisitem.author.orcid0000-0002-2924-1225-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameCarmona Duarte, María Cristina-
crisitem.author.fullNameDíaz Cabrera, Moisés-
crisitem.author.fullNameFerrer Ballester, Miguel Ángel-
crisitem.project.principalinvestigatorCarmona Duarte, María Cristina-
crisitem.event.eventsstartdate16-10-2023-
crisitem.event.eventsenddate19-10-2023-
Appears in Collections:Actas de congresos
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