000131279 001__ 131279
000131279 005__ 20240207154753.0
000131279 0247_ $$2doi$$a10.1016/j.ijar.2019.07.012
000131279 0248_ $$2sideral$$a114133
000131279 037__ $$aART-2019-114133
000131279 041__ $$aeng
000131279 100__ $$0(orcid)0000-0002-7581-0345$$aHuitzil, Ignacio
000131279 245__ $$aGait recognition using fuzzy ontologies and Kinect sensor data
000131279 260__ $$c2019
000131279 5203_ $$aGait recognition involves the automatic classification of human people from sequences of data about their movement patterns. It is an interesting problem with several applications, such as security or medicine. Even low cost sensors can be used to capture pose sequences with enough quality to make a successful classification possible. In this paper, we describe the use of fuzzy ontologies to represent sequences of Microsoft Kinect gait data and some biometric features relevant for the gait recognition computed after them, enabling more reusable and interpretable datasets. We also propose a novel recognition algorithm based on fuzzy logic that outperforms state-of-the-art methods for straight line walks. We also face the problem of the identification of unknown individuals that are not present in the system knowledge base.
000131279 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-78011-C4$$9info:eu-repo/grantAgreement/ES/UZ/CUD2017-17
000131279 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000131279 590__ $$a2.678$$b2019
000131279 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b55 / 136 = 0.404$$c2019$$dQ2$$eT2
000131279 592__ $$a0.8$$b2019
000131279 593__ $$aSoftware$$c2019$$dQ1
000131279 593__ $$aTheoretical Computer Science$$c2019$$dQ2
000131279 593__ $$aApplied Mathematics$$c2019$$dQ2
000131279 593__ $$aArtificial Intelligence$$c2019$$dQ2
000131279 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000131279 700__ $$0(orcid)0000-0002-9169-5287$$aDranca, Lacramiora
000131279 700__ $$0(orcid)0000-0001-8531-353X$$aBernad, Jorge$$uUniversidad de Zaragoza
000131279 700__ $$0(orcid)0000-0001-5136-4152$$aBobillo, Fernando$$uUniversidad de Zaragoza
000131279 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000131279 773__ $$g113 (2019), 354-371$$pInt. j. approx. reason.$$tINTERNATIONAL JOURNAL OF APPROXIMATE REASONING$$x0888-613X
000131279 8564_ $$s1736251$$uhttps://zaguan.unizar.es/record/131279/files/texto_completo.pdf$$yPostprint
000131279 8564_ $$s1170372$$uhttps://zaguan.unizar.es/record/131279/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000131279 909CO $$ooai:zaguan.unizar.es:131279$$particulos$$pdriver
000131279 951__ $$a2024-02-07-14:36:47
000131279 980__ $$aARTICLE