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Predictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learning

dc.contributor.authorGarcía-Restrepo, Johannaspa
dc.contributor.authorAriza Colpas, Paola Patriciaspa
dc.contributor.authorOñate-Bowen, Alvaro Agustínspa
dc.contributor.authorSuarez Brieva, Eydyspa
dc.contributor.authorUrina-Triana, Miguelspa
dc.contributor.authorDe-La-Hoz-Franco, Emirospa
dc.contributor.authorDíaz-Martínez, Jorge Luisspa
dc.contributor.authorButt Shariq, Azizspa
dc.contributor.authorMolina Estren, Diegospa
dc.date.accessioned2021-09-14T16:21:56Z
dc.date.available2021-09-14T16:21:56Z
dc.date.issued2021
dc.identifier.issn1877-0509spa
dc.identifier.urihttps://hdl.handle.net/11323/8691spa
dc.description.abstractAI-based techniques have included countless applications within the engineering field. These range from the automation of important procedures in Industry and companies, to the field of Process Control. Smart Home (SH) technology is designed to help house residents improve their daily activities and therefore enrich the quality of life while preserving their privacy. An SH system is usually equipped with a collection of software interrelated with hardware components to monitor the living space by capturing the behavior of the resident and their occupations. By doing so, the system can report risks, situations, and act on behalf of the resident to their satisfaction. This research article shows the experimentation carried out with the human activity recognition dataset, CASAS Kyoto, through preprocessing and cleaning processes of the data, showing the Vía Regression classifier as an excellent option to process this type of data with an accuracy 99.7% effectivespa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceProcedia Computer Sciencespa
dc.subjectHARspa
dc.subjectHuman activity recognitionspa
dc.subjectMachine learningspa
dc.subjectADLspa
dc.subjectActivity daily livingspa
dc.titlePredictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learningspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050921014721spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2021.07.069spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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