{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119691"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119691","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Automatic 'Timed-Up and Go' (TUG) test segmentation","abstract":"The 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.","abstract_html":"The 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.","abstract_has_math":false,"creators":["Green, Ari M"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 44-45)."]},{"key":"dc:description.abstract","label":"Abstract","values":["The 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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Automatic 'Timed-Up and Go' (TUG) test segmentation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Randall Davis."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Green, Ari M"],"dc:date.accessioned":["2018-12-18T19:45:58Z"],"dc:date.available":["2018-12-18T19:45:58Z"],"dc:date.issued":["2018"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 44-45)."],"dc:description.abstract":["The 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."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/119691"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. 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