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dc.contributor.advisorPopescu, Mihail, 1962-eng
dc.contributor.authorMahnot, Abhishekeng
dc.date.issued2009eng
dc.date.submitted2009 Summereng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on September 22, 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.descriptionThesis advisor: Dr. Mihail Popescu.eng
dc.descriptionM.S. University of Missouri--Columbia 2009.eng
dc.description.abstractWith the increasing of elderly population, there are more and more health problems occurring in everyday activities among this group of people. An investigation shows many elderly people get injures or trigger more serious health problems due to falling on the floor at their home or hospitals without artificial monitoring. There are many techniques to monitor the fall remotely and provide assistance as soon as possible. For this purpose video cameras are deployed at the place of living of an elderly but his might lead to an uncomfortable feeling of being spied on, hence we try to use just the sound (mainly frequency domain features) instead of video to detect a fall remotely. Sound signal is collected for a falling person along with normal everyday sounds, a classifier is trained using these sounds and using these classifiers we try to do the classification of an unknown sound as fall or non-fall. The next problem though is how to collect exact sound sample of a falling person as that is the first thing we want to avoid. In this work therefore we try to train our classifier using data from only one of the two classes which is the sound samples of normal everyday sounds only. These classifiers are called one class classifiers. We compare the performance of these classifiers with the conventional two class classifiers which uses examples from both the classes by testing both on the same dataset. Acoustic feature that we use to do classification is Mel Frequency Cepstrum coefficients or MFCC in short. We also test other spectrum based features like Energy Ratio Sub-band, Band-width and centroid Frequency.eng
dc.description.bibrefIncludes bibliographical references.eng
dc.format.extentix, 80 pageseng
dc.identifier.oclc698110297eng
dc.identifier.urihttps://doi.org/10.32469/10355/9744eng
dc.identifier.urihttps://hdl.handle.net/10355/9744
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2009 Theseseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshPersonal emergency response systemseng
dc.subject.lcshPatient monitoringeng
dc.subject.lcshOlder people -- Health risk assessmenteng
dc.subject.lcshFalls (Accidents) -- Mathematical modelseng
dc.subject.lcshOlder people -- Care -- Mathematical modelseng
dc.titleFall detection using acoustic features and one class classifierseng
dc.typeThesiseng
thesis.degree.disciplineElectrical and computer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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