dc.contributor.author | Li, Stephen,
M. Eng.
Massachusetts Institute of Technology. | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2019-12-05T18:10:23Z | |
dc.date.available | 2019-12-05T18:10:23Z | |
dc.date.copyright | 2019 | en_US |
dc.date.issued | 2019 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/123202 | en_US |
dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
dc.description | Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 | en_US |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (page 53). | en_US |
dc.description.abstract | Early detection of neurodegenerative diseases can lead to slower disease progression, as well as possible symptom reduction. Existing research has studied how cognitively impaired subjects solve tests such as the clock-drawing test and the Digital Symbol-Digit Test differently compared to healthy subjects. While subjects in previous work used a digitized pen in solving the Digital Symbol-Digit test, our research focuses on having the subjects wear eye-tracking glasses as well. These glasses bring a significant improvement in mobility over computer-mounted or headframe eye trackers, but also may come with its reliability issues. After these issues are solved, the gaze data provided brings a wealth of information on learning rate and clues to what the subject is thinking. | en_US |
dc.description.statementofresponsibility | by Stephen Li. | en_US |
dc.format.extent | 53 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Eye tracking for cognition | en_US |
dc.type | Thesis | en_US |
dc.description.degree | M. Eng. in Computer Science and Engineering | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.identifier.oclc | 1129252378 | en_US |
dc.description.collection | M. Eng. in Computer Science and Engineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
dspace.imported | 2023-03-08T20:25:31Z | en_US |