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dc.contributor.advisorTrevor Darrell.en_US
dc.contributor.authorKo, Teresa H., 1980-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2006-03-24T16:14:04Z
dc.date.available2006-03-24T16:14:04Z
dc.date.copyright2003en_US
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/29674
dc.descriptionThesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.en_US
dc.descriptionIncludes bibliographical references (p. 55-58).en_US
dc.description.abstractThis thesis used machine learning techniques to extract useful information about human body articulations. First, it presents a learning approach to model non-linear constraints; a support vector classifier is trained from motion capture data to model the boundary of the space of valid poses. Next, it proposes a system that incorporates body tracking and gesture recognition for an untethered human-computer interface. The detection step utilizes an SVM to identify periods of gesture activity. The classification step uses gesture-specific Hidden Markov Models (HMMs) to determine which gesture was performed at any time period, and to extract the parameters of those gestures. Several experiments were performed to verify the effectiveness of these techniques with encouraging results.en_US
dc.description.statementofresponsibilityby Teresa H. Ko.en_US
dc.format.extent58 p.en_US
dc.format.extent2053608 bytes
dc.format.extent2053416 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleUntethered human motion recognition for a multimodal interfaceen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc53833690en_US


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