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Sensors 2013, 13(11), 15434-15451; doi:10.3390/s131115434
Article

Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes

1,2,* , 3
, 1
 and 1,2
1 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, Valencia 46022, Spain 2 Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain 3 Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
* Author to whom correspondence should be addressed.
Received: 14 September 2013 / Revised: 30 October 2013 / Accepted: 4 November 2013 / Published: 11 November 2013
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Abstract

The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection.
Keywords: process mining; individualized behavior modeling; ambient assisted living; ILS processing process mining; individualized behavior modeling; ambient assisted living; ILS processing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fernández-Llatas, C.; Benedi, J.-M.; García-Gómez, J.M.; Traver, V. Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors 2013, 13, 15434-15451.

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