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dc.creatorAriza Colpas, Paola Patricia
dc.creatorVICARIO, ENRICO
dc.creatorDe-La-Hoz-Franco, Emiro
dc.creatorPineres-Melo, Marlon
dc.creatorOviedo Carrascal, Ana Isabel
dc.creatorPATARA, FULVIO
dc.date.accessioned2020-11-19T14:39:51Z
dc.date.available2020-11-19T14:39:51Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7356
dc.description.abstractCurrently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledgespa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.relation.ispartofhttps://www.mdpi.com/journal/sensors/special_issues/human-machinespa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourcesensorsspa
dc.subjectambient assisted living—AALspa
dc.subjecthuman activity recognition—HARspa
dc.subjectactivities of dailyliving—ADLspa
dc.subjectctivity recognition systems—ARSspa
dc.subjectclusteringspa
dc.subjectunsupervised activity recognitionspa
dc.titleUnsupervised Human Activity Recognition Using the Clustering Approach: A Reviewspa
dc.typearticlespa
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