Individual Behavior Modeling with Sensors Using Process Mining

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dc.catalogadorgrr
dc.contributor.authorDogan, Onur
dc.contributor.authorMartinez-Millana, Antonio
dc.contributor.authorRojas, Eric
dc.contributor.authorSepúlveda Fernández, Marcos Ernesto
dc.contributor.authorMuñoz Gama, Jorge
dc.contributor.authorTraver, Vicente
dc.contributor.authorFernandez-Llatas, Carlos
dc.date.accessioned2024-01-18T13:36:58Z
dc.date.available2024-01-18T13:36:58Z
dc.date.issued2019
dc.description.abstractUnderstanding human behavior can assist in the adoption of satisfactory healthinterventions and improved care. One of the main problems relies on the definition of humanbehaviors, as human activities depend on multiple variables and are of dynamic nature. Althoughsmart homes have advanced in the latest years and contributed to unobtrusive human behaviortracking, artificial intelligence has not coped yet with the problem of variability and dynamismof these behaviors. Process mining is an emerging discipline capable of adapting to the nature ofhigh-variate data and extract knowledge to define behavior patterns. In this study, we analyze datafrom 25 in-house residents acquired with indoor location sensors by means of process miningclustering techniques, which allows obtaining workflows of the human behavior inside the house.Data are clustered by adjusting two variables: the similarity index and the Euclidean distancebetween workflows. Thereafter, two main models are created: (1) a workflow view to analyze thecharacteristics of the discovered clusters and the information they reveal about human behaviorand (2) a calendar view, in which common behaviors are rendered in the way of a calendarallowing to detect relevant patterns depending on the day of the week and the season of the year.Three representative patients who performed three different behaviors: stable, unstable, and complexbehaviors according to the proposed approach are investigated. This approach provides humanbehavior details in the manner of a workflow model, discovering user paths, frequent transitionsbetween rooms, and the time the user was in each room, in addition to showing the results into thecalendar view increases readability and visual attraction of human behaviors, allowing to us detectpatterns happening on special days.
dc.fechaingreso.objetodigital2024-01-18
dc.format.extent17 páginas
dc.fuente.origenORCID-ene24
dc.identifier.doi10.3390/electronics8070766
dc.identifier.issn0013-5070
dc.identifier.scopusidSCOPUS_ID:85070945209
dc.identifier.urihttps://doi.org/10.3390/electronics8070766
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/80607
dc.identifier.wosidWOS:000482063200034
dc.information.autorucEscuela de Ingeniería; Rojas, Eric ; 0000-0002-2570-1861; 224862
dc.information.autorucEscuela de Ingeniería; Muñoz Gama, Jorge; 0000-0002-6908-3911; 1013863
dc.information.autorucEscuela de Ingeniería; Sepúlveda Fernández, Marcos Ernesto; 0000-0002-9467-7666; 80415
dc.issue.numero7
dc.language.isoen
dc.nota.accesoContenido completo
dc.revistaElectronics
dc.rightsacceso abierto
dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectBehavior models
dc.subjectProcess mining
dc.subjectIndoor location system
dc.subjectSensors
dc.subjectSmart homes
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleIndividual Behavior Modeling with Sensors Using Process Mining
dc.typeartículo
dc.volumen8
sipa.codpersvinculados249881
sipa.codpersvinculados224862
sipa.codpersvinculados1013863
sipa.codpersvinculados80415
sipa.trazabilidadORCID;2024-01-15
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