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dc.contributor.advisorKeller, James M.eng
dc.contributor.advisorSkubic, Margeeng
dc.contributor.authorAnderson, Derek T., 1979-eng
dc.date.issued2010eng
dc.date.submitted2010 Summereng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on August 16, 2010).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. James Keller and Dr. Marjorie Skubic.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri--Columbia 2010.eng
dc.description.abstractThe thesis advanced herein is that linguistic summarization is essential for the reliable succinct modeling and inference of human activity. It is also asserted that the inherent and unavoidable uncertainty is linguistic and fuzzy. Advantages of the proposed work include the generation of human interpretable confidence values, improved rejection of unknown activity, information reduction, complexity management, and the recognition of adverse events. Specifically, a computer vision-based hierarchical soft-computing linguistic summarization framework is proposed. First, images are summarized through the identification of a human and a three-dimensional object called voxel person is constructed. Next, approximate reasoning is used to linguistically summarize the state of the human at each moment, i.e. image, using features extracted from voxel person. Subsequently, temporal linguistic summarizations are produced from the state membership time series. State summaries are used to infer activity, which are also linguistically summarized and subsequently used in a hierarchical similar fashion to recognize additional specific types of higher level activity. A system comprised of two levels is described for the goal of elderly activity recognition. The system parameters are designed under the supervision of nurses. The results are compared to probabilistic graphical models for three data sets consisting of student and nurse trained and supervised stunt actor activities.eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.format.extentxiv, 198 pageseng
dc.identifier.merlinb80170420eng
dc.identifier.oclc668415807eng
dc.identifier.urihttps://hdl.handle.net/10355/8878
dc.identifier.urihttps://doi.org/10.32469/10355/8878eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshHuman activity recognitioneng
dc.subject.lcshOlder peopleeng
dc.subject.lcshAndroidseng
dc.subject.lcshFuzzy systemseng
dc.subject.lcshData miningeng
dc.subject.lcshCyberneticseng
dc.subject.lcshSpatial behavioreng
dc.subject.lcshPattern recognition systemseng
dc.titleLinguistic summarization of human activityeng
dc.typeThesiseng
thesis.degree.disciplineElectrical and computer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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