Individual behavior modeling with sensors using process mining

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dc.authoridMartinez-Millana, Antonio / 0000-0003-1056-5067
dc.authoridDOGAN, ONUR / 0000-0003-3543-4012
dc.authoridRojas, Eric / 0000-0002-2570-1861
dc.authoridSepulveda, Marcos / 0000-0002-9467-7666
dc.authoridTraver, Vicente / 0000-0003-1806-8575
dc.authoridFernandez-Llatas, Carlos / 0000-0002-2819-5597
dc.authorscopusid57202924825
dc.authorscopusid49964119000
dc.authorscopusid25636372400
dc.authorscopusid7005859415
dc.authorscopusid36603094000
dc.authorscopusid36947073600
dc.authorscopusid57204151458
dc.authorwosidMartinez-Millana, Antonio/O-8012-2015
dc.authorwosidDogan, Onur/AAN-3208-2021
dc.authorwosidTraver, Vicente/B-8139-2015
dc.authorwosidDOGAN, ONUR/ABI-4575-2020
dc.authorwosidFernandez-Llatas, Carlos/B-2957-2013
dc.contributor.authorDoğan, Onur
dc.contributor.authorMartinez-Millana, Antonio
dc.contributor.authorRojas, Eric
dc.contributor.authorSepulveda, Marcos
dc.contributor.authorMunoz-Gama, Jorge
dc.contributor.authorTraver, Vicente
dc.contributor.authorFernandez-Llatas, Carlos
dc.date.accessioned2022-02-15T16:57:34Z
dc.date.available2022-02-15T16:57:34Z
dc.date.issued2019
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractUnderstanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering 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 distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing 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 complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.en_US
dc.description.sponsorshipITACA SABIEN; CONICYTComision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) [REDI 170136]en_US
dc.description.sponsorshipThis research was funded by ITACA SABIEN and partially supported by CONICYT REDI 170136.en_US
dc.identifier.doi10.3390/electronics8070766
dc.identifier.issn2079-9292
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85070945209en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/electronics8070766
dc.identifier.urihttps://hdl.handle.net/20.500.14034/206
dc.identifier.volume8en_US
dc.identifier.wosWOS:000482063200034en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.journalElectronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectbehavior modelsen_US
dc.subjectprocess miningen_US
dc.subjectindoor location systemen_US
dc.subjectsmart homesen_US
dc.subjectsensorsen_US
dc.subjectHuman Activity Recognitionen_US
dc.subjectInterneten_US
dc.subjectFrameworken_US
dc.subjectTrackingen_US
dc.subjectSystemen_US
dc.subjectThingsen_US
dc.subjectIoten_US
dc.titleIndividual behavior modeling with sensors using process miningen_US
dc.typeArticleen_US

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