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dc.contributor.authorLi, Stephen, M. Eng. Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-12-05T18:10:23Z
dc.date.available2019-12-05T18:10:23Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123202en_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.descriptionThesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 53).en_US
dc.description.abstractEarly 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.statementofresponsibilityby Stephen Li.en_US
dc.format.extent53 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleEye tracking for cognitionen_US
dc.typeThesisen_US
dc.description.degreeM. Eng. in Computer Science and Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1129252378en_US
dc.description.collectionM. Eng. in Computer Science and Engineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2023-03-08T20:25:31Zen_US


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