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Individual Behavior Modeling with Sensors Using Process Mining

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Individual Behavior Modeling with Sensors Using Process Mining

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dc.contributor.author Dogan, Onur es_ES
dc.contributor.author Martinez-Millana, Antonio es_ES
dc.contributor.author Rojas, Eric es_ES
dc.contributor.author Sepulveda, Marcos es_ES
dc.contributor.author Munoz Gama, Jorge es_ES
dc.contributor.author Traver Salcedo, Vicente es_ES
dc.contributor.author Fernández Llatas, Carlos es_ES
dc.date.accessioned 2020-07-15T03:32:32Z
dc.date.available 2020-07-15T03:32:32Z
dc.date.issued 2019-07-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/148008
dc.description.abstract [EN] Understanding 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. es_ES
dc.description.sponsorship This research was funded by ITACA SABIEN and partially supported by CONICYT REDI 170136. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Electronics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Behavior models es_ES
dc.subject Process mining es_ES
dc.subject Indoor location system es_ES
dc.subject Smart homes es_ES
dc.subject Sensors es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Individual Behavior Modeling with Sensors Using Process Mining es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/electronics8070766 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CONICYT//REDI 170136/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Dogan, O.; Martinez-Millana, A.; Rojas, E.; Sepulveda, M.; Munoz Gama, J.; Traver Salcedo, V.; Fernández Llatas, C. (2019). Individual Behavior Modeling with Sensors Using Process Mining. Electronics. 8(7):1-17. https://doi.org/10.3390/electronics8070766 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/electronics8070766 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 7 es_ES
dc.identifier.eissn 2079-9292 es_ES
dc.relation.pasarela S\391047 es_ES
dc.contributor.funder ITACA SABIEN es_ES
dc.contributor.funder Comisión Nacional de Investigación Científica y Tecnológica, Chile es_ES
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