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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

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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
dc.contributor.authorGarcía-Restrepo, Johanna
dc.contributor.authorAriza-Colpas, Paola Patricia
dc.contributor.authorOñate-Bowen, Alvaro Agustín
dc.contributor.authorSuarez-Brieva, Eydy del Carmen
dc.contributor.authorUrina-Triana, Miguel
dc.contributor.authorDe-la-Hoz-Franco, Emiro
dc.contributor.authorDíaz-Martínez, Jorge Luis
dc.contributor.authorButt, Shariq Aziz
dc.contributor.authorMolina_Estren, Diego
dc.date.accessioned2021-10-01T21:25:05Z
dc.date.available2021-10-01T21:25:05Z
dc.date.issued2021
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% effectiveeng
dc.format.mimetypepdfspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2021.07.069
dc.identifier.issn18770509
dc.identifier.urihttps://hdl.handle.net/20.500.12442/8604
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1877050921014721?via%3Dihub
dc.language.isoengeng
dc.publisherElseviereng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceProcedia Computer Scienceeng
dc.sourceVol. 191 (2021)
dc.subjectHAReng
dc.subjectHuman Activity Recognitioneng
dc.subjectMachine Learningeng
dc.subjectADLeng
dc.subjectActivity Daily Livingeng
dc.titlePredictive model for the identification of activities of daily living (ADL) in indoor environments using classification techniques based on Machine Learningeng
dc.type.driverinfo:eu-repo/semantics/articleeng
dc.type.spaArtículo científicospa
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