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Individual Behavior Modeling with Sensors Using Process Mining

Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey
Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain
Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile
Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politecnico La Fe, Bulevar Sur S/N, 46026 Valencia, Spain
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 766;
Received: 3 June 2019 / Revised: 2 July 2019 / Accepted: 3 July 2019 / Published: 9 July 2019
(This article belongs to the Special Issue Recent Machine Learning Applications to Internet of Things (IoT))
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. View Full-Text
Keywords: behavior models; process mining; indoor location system; smart homes; sensors behavior models; process mining; indoor location system; smart homes; sensors
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MDPI and ACS Style

Dogan, O.; Martinez-Millana, A.; Rojas, E.; Sepúlveda, M.; Munoz-Gama, J.; Traver, V.; Fernandez-Llatas, C. Individual Behavior Modeling with Sensors Using Process Mining. Electronics 2019, 8, 766.

AMA Style

Dogan O, Martinez-Millana A, Rojas E, Sepúlveda M, Munoz-Gama J, Traver V, Fernandez-Llatas C. Individual Behavior Modeling with Sensors Using Process Mining. Electronics. 2019; 8(7):766.

Chicago/Turabian Style

Dogan, Onur, Antonio Martinez-Millana, Eric Rojas, Marcos Sepúlveda, Jorge Munoz-Gama, Vicente Traver, and Carlos Fernandez-Llatas. 2019. "Individual Behavior Modeling with Sensors Using Process Mining" Electronics 8, no. 7: 766.

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