Type-1 and type-2 fuzzy systems for detecting visitors in an uncertain environment

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Type-1 and type-2 fuzzy systems for detecting visitors in an uncertain environment

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dc.contributor.advisor Skubic, Marge en_US
dc.contributor.author Reed, Kevin W. en_US
dc.date.accessioned 2010-03-09T16:05:03Z
dc.date.available 2010-03-09T16:05:03Z
dc.date.issued 2009 en_US
dc.date.submitted 2009 Summer en_US
dc.identifier.other ReedK-070109-T277 en_US
dc.identifier.uri http://hdl.handle.net/10355/6478
dc.description Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 18, 2010). en_US
dc.description The 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. en_US
dc.description Thesis advisor: Dr. Marjorie Skubic. en_US
dc.description Includes bibliographical references. en_US
dc.description M.S. University of Missouri--Columbia 2009. en_US
dc.description Dissertations, Academic -- University of Missouri--Columbia -- Computer engineering. en_US
dc.description.abstract In this work, I have developed an algorithm to detect the presence of visitors in a noninvasive manner. This algorithm is designed as part of an in home monitoring system. The data from the algorithm will be used as a way to monitor the social health of the resident. It will also be used to help isolate the times when the resident is in the apartment alone, so that parameters like activity levels can be calculated. Type-1 and Type-2 fuzzy systems are compared for classification performance. A series of lab tests provided the information necessary to model the motion sensors. Results from the motion sensor tests are used as guides for the Footprint of Uncertainty (FOU) used in the Type-2 systems. It is shown that the FOU values do not significantly impact the classification accuracy of the Type-2 systems. Classification accuracy of the ground truth data collected in a test apartment reached 88%. Additionally, the Type-2 MISO 2 Agent and Type-2 SISO systems best identify the known visitor times in the resident apartments. Ongoing human subject monitoring data is evaluated empirically. The results from an organized set of tests in a test apartment are presented. en_US
dc.format.extent xi, 137 pages en_US
dc.language.iso en_US en_US
dc.publisher University of Missouri--Columbia en_US
dc.relation.ispartof 2009 Freely available theses (MU) en_US
dc.subject.lcsh Dwellings -- Security measures en_US
dc.subject.lcsh Life care communities -- Security measures en_US
dc.subject.lcsh Fuzzy systems en_US
dc.subject.lcsh Algorithms -- Design en_US
dc.subject.lcsh Motion control devices en_US
dc.title Type-1 and type-2 fuzzy systems for detecting visitors in an uncertain environment en_US
dc.type Thesis en_US
thesis.degree.discipline Computer engineering en_US
thesis.degree.grantor University of Missouri--Columbia en_US
thesis.degree.name M.S. en_US
thesis.degree.level Masters en_US
dc.identifier.oclc 537671693 en_US
dc.relation.ispartofcommunity University of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2009 Theses


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