Automatic 'Timed-Up and Go' (TUG) test segmentation
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119691 |
| _version_ | 1826199851749081088 |
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| author | Green, Ari M |
| author2 | Randall Davis. |
| author_facet | Randall Davis. Green, Ari M |
| author_sort | Green, Ari M |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T11:26:47Z |
| format | Thesis |
| id | mit-1721.1/119691 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:26:47Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1196912019-04-12T17:32:14Z Automatic 'Timed-Up and Go' (TUG) test segmentation Automatic TUG test segmentation Green, Ari M Randall Davis. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Ari M. Green. M. Eng. 2018-12-18T19:45:58Z 2018-12-18T19:45:58Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119691 1078148969 eng MIT 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. http://dspace.mit.edu/handle/1721.1/7582 45 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Green, Ari M Automatic 'Timed-Up and Go' (TUG) test segmentation |
| title | Automatic 'Timed-Up and Go' (TUG) test segmentation |
| title_full | Automatic 'Timed-Up and Go' (TUG) test segmentation |
| title_fullStr | Automatic 'Timed-Up and Go' (TUG) test segmentation |
| title_full_unstemmed | Automatic 'Timed-Up and Go' (TUG) test segmentation |
| title_short | Automatic 'Timed-Up and Go' (TUG) test segmentation |
| title_sort | automatic timed up and go tug test segmentation |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119691 |
| work_keys_str_mv | AT greenarim automatictimedupandgotugtestsegmentation AT greenarim automatictugtestsegmentation |