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dc.contributor.advisorSkubic, Marjorieeng
dc.contributor.authorBanerjee, Tanvieng
dc.date.issued2010eng
dc.date.submitted2010 Springeng
dc.descriptionThe entire thesis text is included in the research.pf file; the official abstract appears in the short.pf file; a non-technical public abstract appears in the public.pf file.eng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on July 8, 2010.)eng
dc.descriptionThesis advisor: Dr. Marjorie Skubic.eng
dc.descriptionM.S. University of Missouri-Columbia 2010.eng
dc.description.abstractIn this study, we present algorithms to segment the activities of sitting and standing, and identify the regions of sit-to-stand transitions in a given image sequence. As a means of fall risk assessment, we propose methods to measure sit-to-stand time using the three dimensional modeling of a human body in vowel space as well as ellipse fitting algorithms and image features to capture orientation of the body. Fuzzy clustering methods such as the Gustafson vessel algorithm are also investigated. The proposed algorithms were tested on 9 subjects with ages ranging from 18 to 88. The classification results were the best for the vowel height with the ellipse fit algorithm at 96.6%; using the vowel height alone gave a classification rate of 86.7%. The comparison was done with the marker-based V icon motion capture system as ground truth as well as a manually controlled stop watch. The average error in sit-to-stand time measurement was the best for vowel voxel height with the ellipse fit technique at 270 ms and worst for vowel voxel height alone at 380 ms. This application can be used as a part of a continuous video monitoring system in the homes of older adults and can provide valuable information which could help detect fall risk and enable them to lead an independent life style for a longer time.eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.identifier.merlinb77767226eng
dc.identifier.oclc646348060eng
dc.identifier.urihttp://hdl.handle.net/10355/8116
dc.identifier.urihttps://doi.org/10.32469/10355/8116eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2010 Theseseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshImaging systemseng
dc.subject.lcshOlder people -- Health risk assessmenteng
dc.subject.lcshPatient monitoringeng
dc.subject.lcshSitting positioneng
dc.subject.lcshHuman mechanicseng
dc.subject.lcshStanding positioneng
dc.subject.lcshFalls (Accidents) -- Mathematical modelseng
dc.titleActivity segmentation with special emphasis on sit-to-stand analysiseng
dc.typeThesiseng
thesis.degree.disciplineElectrical engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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