Detecting Multimodal Behaviors for Neurodegenerative Disease
Name
berrones-antoniob-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
4.43 MB
Format
Adobe PDF
Checksum (MD5)
801b7f31a14c30620b9ea2585a5d77a7
Author(s)
Berrones, Antonio
Advisor(s)
Davis, Randall
Penney, Dana
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
Abstract
Neurodegenerative diseases such as Parkinson’s and Alzheimer’s are incurable and affect millions of people worldwide. Early diagnosis is critical for improving quality of life for patients. Current methods rely on the use of tests administered and evaluated by clinicians. The digital Symbol Digit Test (dSDT) is a novel cognitive test that aims to distinguish between individuals with normal and impaired cognitive abilities. This thesis will develop a framework for processing collected participant eye-tracking and handwriting data and show its use in detecting specific multimodal learning behaviors. Furthermore, this thesis will explore recommendations for working with eye-tracking systems and outline future steps towards developing a multimodal classification model to automate early diagnosis of neurodegenerative disease.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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