Regular graphical pattern detection and its applications
Name
1193032064-MIT.pdf
Size
29.93 MB
Format
Adobe PDF
Checksum (MD5)
5a89d6663eab2ffb9b2eff75c695b122
Author(s)
Wu, Shang-Yun(Shang-Yun Maggie)
Advisor(s)
Randall Davis and Dana L. Penney.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
While there is no existing cure to Alzheimer's disease, early detection and intervention can greatly improve patient prognosis. However, early signals can be very subtle changes in behaviors. Our research aims to understand an individual's behaviors through analyzing their gaze patterns. We do this by introducing eye tracking in addition to traditional pen-and-paper tests that measure cognitive status. Traditionally, fiducial markers are added to assist in locating gaze positions with respect to an object in the real world. However, fiducial markers can introduce a distraction and make the test different from its traditional pen-and-paper version. To enable eye tracking without fiducial markers, we present an algorithm that identifies the graphics within the test, allowing us to locate a subject's gaze on the test form using the test alone. It is a novel approach to detecting features in regular graphical patterns despite occlusions.
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 (pages 65-66).
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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