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dc.contributor.advisorRandall Davis.en_US
dc.contributor.authorGreen, Ari Men_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-12-18T19:45:58Z
dc.date.available2018-12-18T19:45:58Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119691
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 44-45).en_US
dc.description.abstractThe Timed-Up and Go test (TUG) is a well-known medical test that is used as an indicator of mental and physical health. I developed the TUG-Segmenter, an automatic segmentation tool that can divide recorded TUG test data into the six main phases of the test: Sitting, Standing-Up, Walking-Forward, Turning, Walking-Back, and Sitting-Down. I created an annotation tool as well that greatly speeds up the creation of ground truth from TUG test data. Using both these tools I was able to evaluate the accuracy of the TUG-Segmenter in terms of the duration of the segmented phases ( 83.4 % accurate ) and the start times of the segmented phases ( 83.6 % accurate). Lastly, I found a 0.3 cm difference for jitteriness and an 8.5 mm/s difference for speed between healthy elderly subjects and healthy young subjects when comparing the features extracted from the individual TUG test phases.en_US
dc.description.statementofresponsibilityby Ari M. Green.en_US
dc.format.extent45 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAutomatic 'Timed-Up and Go' (TUG) test segmentationen_US
dc.title.alternativeAutomatic TUG test segmentationen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1078148969en_US


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