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Modelo predictivo para el reconocimiento de actividades humanas basado en técnicas de Machine Learning y de selección de características

dc.contributor.advisorDe-La-Hoz-Franco, Emirospa
dc.contributor.advisorDiaz Martínez, Jorgespa
dc.contributor.authorPatiño Saucedo, Janns Álvarospa
dc.date.accessioned2021-05-12T18:22:03Z
dc.date.available2021-05-12T18:22:03Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/11323/8249spa
dc.description.abstractAmbient assisted living (AAL), focus on generating innovative products and services in order to aid and medical attention to elderly people who suffer from neurodegenerative diseases or a disability. This research area is responsible for the development of activity recognition systems (ARS) which are based on Human Activity Recognition (HAR), specifically in activities of daily life (ADL) in indoor environments. These systems make it possible to identify the type of activity that people carry out, offering a possibility of effective assistance that allows them to carry out daily activities with total normality. The performance of the ARS in the HAR process must be evaluated through the approach of experimental scenarios with data sets available by the scientific community in online repositories, this work proposes a variety of combinations of machine learning algorithms with feature selection algorithms, obtaining as a result a functional model for the HAR, which combines the classification algorithm Logistic model trees (LMT) and the feature selection algorithm One R.eng
dc.description.abstractLos ambientes asistidos para la vida - AAL por sus siglas en inglés (Ambient Assisted Living), se enfocan en generar productos y servicios innovadores en aras de proporcionar asistencia y atención médica a personas de avanzada edad que padezcan enfermedades neurodegenerativas o alguna discapacidad. Esta área de investigación se encarga del desarrollo de sistemas para el reconocimiento de actividad - ARS (Activity Recognition Systems) los cuales están basados en el reconocimiento de actividades humanas - HAR (Human Activity Recognition), específicamente en actividades de la vida diaria - ADL (Activities of Daily Living) en ambientes interiores (indoor). Estos sistemas permiten identificar el tipo de actividad que realizan las personas, ofreciendo una posibilidad de asistencia efectiva que les permita llevar a cabo actividades cotidianas con total normalidad. El desempeño de los ARS en el proceso de HAR, debe ser evaluado a través del planteamiento de escenarios experimentales con conjuntos de datos dispuestos por la comunidad científica en repositorios en linea, este trabajo plantea una variedad de combinaciones de técnicas de machine learning con técnicas de selección de características, obteniendo como resultado un modelo funcional para el HAR, que combina la técnica de clasificación árboles para el modelamiento logístico - LMT por sus siglas en inglés (Logistic Model Trees) y la técnica de selección de características One R.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.subjectHuman Activity Recognition (HAR)eng
dc.subjectMachine learningeng
dc.subjectClassificationeng
dc.subjectFeature selectioneng
dc.subjectReconocimiento de Actividades Humanas (HAR)spa
dc.subjectAprendizaje automáticospa
dc.subjectClasificaciónspa
dc.subjectSelección de característicasspa
dc.titleModelo predictivo para el reconocimiento de actividades humanas basado en técnicas de Machine Learning y de selección de característicasspa
dc.typeTrabajo de grado - Maestríaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.programMaestría en Ingeniería con Énfasis en Sistemasspa
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