Fernández Llatas, C.; Benedí Ruiz, JM.; García Gómez, JM.; Traver Salcedo, V. (2013). Process mining for individualised behaviour modeling using wireless tracking in nursing homes. Sensors. 13(11):15434-15451. doi:10.3390/s131115434
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/43741
Title:
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Process mining for individualised behaviour modeling using wireless tracking in nursing homes
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Author:
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Fernández Llatas, Carlos
Benedí Ruiz, José Miguel
García Gómez, Juan Miguel
Traver Salcedo, Vicente
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UPV Unit:
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Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada
Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
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Issued date:
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Abstract:
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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 ...[+]
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
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Subjects:
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Process mining
,
Individualized behavior modeling
,
Ambient assisted living
,
ILS processing
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Copyrigths:
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Reconocimiento (by)
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Source:
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Sensors. (issn:
1424-8220
)
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DOI:
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10.3390/s131115434
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Publisher:
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MDPI
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Publisher version:
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http://dx.doi.org/10.3390/s131115434
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Project ID:
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Spanish Government eMotiva Project (TSI-020110-2009-219)
Health Institute Carlos III through the RETICSCombiomed (RD07/0067/2001)
Ministerio de Educación y Ciencia Programa Torres Quevedo (PTQ05-02-03386)
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Thanks:
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The authors want to acknowledge the Spanish Government, the eMotiva Project (TSI-020110-2009-219) partners, Health Institute Carlos III through the RETICSCombiomed (RD07/0067/2001) and Programa Torres Quevedo from Ministerio ...[+]
The authors want to acknowledge the Spanish Government, the eMotiva Project (TSI-020110-2009-219) partners, Health Institute Carlos III through the RETICSCombiomed (RD07/0067/2001) and Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ05-02-03386), for their support and the professionals and residents of Centro Residencial San Sebastian en la Pobla De Vallbona and MySphera Enterprise for their active participation in the project.
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Type:
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Artículo
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