Mostrar el registro sencillo del ítem
dc.contributor.author | Fernández Llatas, Carlos | es_ES |
dc.contributor.author | Benedí Ruiz, José Miguel | es_ES |
dc.contributor.author | García Gómez, Juan Miguel | es_ES |
dc.contributor.author | Traver Salcedo, Vicente | es_ES |
dc.date.accessioned | 2014-10-30T16:38:36Z | |
dc.date.available | 2014-10-30T16:38:36Z | |
dc.date.issued | 2013-11 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10251/43741 | |
dc.description.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 | es_ES |
dc.description.sponsorship | 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. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Process mining | es_ES |
dc.subject | Individualized behavior modeling | es_ES |
dc.subject | Ambient assisted living | es_ES |
dc.subject | ILS processing | es_ES |
dc.subject.classification | FISICA APLICADA | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.subject.classification | TECNOLOGIA ELECTRONICA | es_ES |
dc.title | Process mining for individualised behaviour modeling using wireless tracking in nursing homes | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s131115434 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MITURCO//TSI-020110-2009-0219/ES/eMOTIVA - Motivación personalizada de pacientes con demencia mediante la detección de patrones de conducta/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//RD07%2F0067%2F2001/ES/RED TEMÁTICA DE INVESTIGACIÓN COOPERATIVA EN BIOMEDICINA COMPUTACIONAL/ / | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MEC//PTQ05-02-03386/ES/PTQ05-02-03386/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica | es_ES |
dc.description.bibliographicCitation | 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. https://doi.org/10.3390/s131115434 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.3390/s131115434 | es_ES |
dc.description.upvformatpinicio | 15434 | es_ES |
dc.description.upvformatpfin | 15451 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 13 | es_ES |
dc.description.issue | 11 | es_ES |
dc.relation.senia | 251633 | |
dc.identifier.pmid | 24225907 | en_EN |
dc.identifier.pmcid | PMC3871075 | en_EN |
dc.contributor.funder | Ministerio de Educación y Ciencia | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | Ministerio de Industria, Turismo y Comercio | es_ES |
dc.description.references | Hobson, P. (1999). The detection of dementia and cognitive impairment in a community population of elderly people with Parkinson’s disease by use of the CAMCOG neuropsychological test. Age and Ageing, 28(1), 39-43. doi:10.1093/ageing/28.1.39 | es_ES |
dc.description.references | Tahan, H. A., & Sminkey, P. V. (2012). Motivational Interviewing. Professional Case Management, 17(4), 164-172. doi:10.1097/ncm.0b013e318253f029 | es_ES |
dc.description.references | Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671 | es_ES |
dc.description.references | Barrachina, S., Bender, O., Casacuberta, F., Civera, J., Cubel, E., Khadivi, S., … Vilar, J.-M. (2009). Statistical Approaches to Computer-Assisted Translation. Computational Linguistics, 35(1), 3-28. doi:10.1162/coli.2008.07-055-r2-06-29 | es_ES |
dc.description.references | Van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., & Weijters, A. J. M. M. (2003). Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering, 47(2), 237-267. doi:10.1016/s0169-023x(03)00066-1 | es_ES |
dc.description.references | De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7 | es_ES |
dc.description.references | Remagnino, P., & Foresti, G. L. (2005). Ambient Intelligence: A New Multidisciplinary Paradigm. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 1-6. doi:10.1109/tsmca.2004.838456 | es_ES |
dc.description.references | Tao Zhao, & Nevatia, R. (2004). Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 1208-1221. doi:10.1109/tpami.2004.73 | es_ES |
dc.description.references | Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2007). Learning and inferring transportation routines. Artificial Intelligence, 171(5-6), 311-331. doi:10.1016/j.artint.2007.01.006 | es_ES |
dc.description.references | Zhou, Y., Law, C. L., Guan, Y. L., & Chin, F. (2011). Indoor Elliptical Localization Based on Asynchronous UWB Range Measurement. IEEE Transactions on Instrumentation and Measurement, 60(1), 248-257. doi:10.1109/tim.2010.2049185 | es_ES |
dc.description.references | Chang, N., Rashidzadeh, R., & Ahmadi, M. (2010). Robust indoor positioning using differential wi-fi access points. IEEE Transactions on Consumer Electronics, 56(3), 1860-1867. doi:10.1109/tce.2010.5606338 | es_ES |
dc.description.references | MySphera Enterprise, RTLS Sphera Indoor Positioning Systemhttp://mysphera.com/ | es_ES |
dc.description.references | Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580. doi:10.1109/5.24143 | es_ES |