Gaze-tracking analysis for cognitive screening and assessment
Name
1193029297-MIT.pdf
Size
3.39 MB
Format
Adobe PDF
Checksum (MD5)
510a26fe3def3e921732313a1c04b440
Author(s)
Sarkar, Sarbari.
Advisor(s)
Randall Davis and Dana L. Penney.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Neurodegenerative diseases degrade the mental and physical capabilities of afflicted individuals around the world. Early diagnosis can make it possible to reduce the effects and progression of these diseases. The novel digital Symbol Digit Test (dSDT) is a new cognitive test that judges patterns of recall and cognitive associations, which can be used to differentiate between cognitive signs displayed by normal and neurologically impaired subjects. Our research identifies different strategies of learning and recall, and automates the process of analyzing the eye-tracking data collected from the dSDT to detect these patterns. This work paves the foundation for future studies to assess differences between healthy and impaired individuals, and model these features to detect and aid in the diagnosis of cognitive states of individuals.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (page 53).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
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