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dc.contributor.advisorDevedžić, Goran
dc.contributor.otherManić, Miodrag-
dc.contributor.otherRistić, Branko
dc.contributor.otherFilipović, Nenad
dc.contributor.otherAdamović, Dragan
dc.creatorPetrović-Savić, Suzana R.
dc.date.accessioned2016-10-29T13:53:17Z
dc.date.available2016-10-29T13:53:17Z
dc.date.available2020-07-03T15:12:33Z
dc.date.issued2016-09-26
dc.identifier.urihttp://nardus.mpn.gov.rs/handle/123456789/6906
dc.identifier.urihttp://eteze.kg.ac.rs/application/showtheses?thesesId=4112
dc.identifier.urihttps://fedorakg.kg.ac.rs/fedora/get/o:716/bdef:Content/download
dc.description.abstractGait is a fundamental human activity. One of the main joints that participate in walking process is knee joint. This joint is considered to be the largest and most complex joint in human body. This complexity comes from possibility of translation and rotation along and around all axes. All of these movements have corresponding pattern. Main purpose of this doctoral thesis is to identify and analyze standard values and patterns of basic movement parameters of healthy individuals. Experimental research was done in Clinical Centre Kragujevac on healthy individuals and on patients with deficient/diseased soft tissue and/or cartilaginous knee structures. Three systems were used for acquiring data – OptiTrack, Kinetic XBOX camera and simple web camera. Mathematical model of a knee was created for calculating identified gait parameters. It is concluded, with a help of statistical methods, that there is a significant difference in gait pattern between healthy individuals and patients with deficient/diseased knee joint structures. For the purpose of getting objective results, models for predicting/classification (based on logistic regression and neural network models) possible damage/illness of knee joint based on walk parameters values and gait curves were created. Models for predicting/classification are valued by diagnostic tests. Results showed that this approach can help in better understanding of processes in knee joint that occur during walking, can help to achieve objectivity in walking process evaluation, improve rehabilitation process depending on level of recovery of the patient, etc.en
dc.formatapplication/pdf
dc.languagesr
dc.publisherУниверзитет у Крагујевцу, Факултет инжењерских наукаsr
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/41007/RS//
dc.rightsopenAccessen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceУниверзитет у Крагујевцуsr
dc.subjectmechanicssr
dc.subjectmehanikaen
dc.subjectsistem za snimanje kretanjaen
dc.subjectparametri kretanja zgloba kolenaen
dc.subjectlogistička regresijaen
dc.subjectneuronske mrežeen
dc.subjectMoCap systemssr
dc.subjectparameters of knee jointsr
dc.subjectlogistic regressionsr
dc.subjectneural networkssr
dc.titleIdentifikacija, analiza i klasifikacija kretanja zgloba kolenasr
dc.typedoctoralThesisen
dc.rights.licenseBY-NC-ND
dcterms.abstractДеведжић, Горан; Филиповић, Ненад; Манић, Миодраг-; Ристић, Бранко; Aдамовић, Драган; Петровић-Савић, Сузана Р.; Идентификација, анализа и класификација кретања зглоба колена; Идентификација, анализа и класификација кретања зглоба колена;
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/48114/Disertacija.pdf
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/48115/bitstream_48115.pdf
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_nardus_6906


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