Detecting Multimodal Behaviors for Neurodegenerative Disease
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 Te...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153898 |
| _version_ | 1811085095528824832 |
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| author | Berrones, Antonio |
| author2 | Davis, Randall |
| author_facet | Davis, Randall Berrones, Antonio |
| author_sort | Berrones, Antonio |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:02:49Z |
| format | Thesis |
| id | mit-1721.1/153898 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:02:49Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1538982024-03-22T03:36:18Z Detecting Multimodal Behaviors for Neurodegenerative Disease Berrones, Antonio Davis, Randall Penney, Dana Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. M.Eng. 2024-03-21T19:14:29Z 2024-03-21T19:14:29Z 2024-02 2024-03-04T16:37:59.424Z Thesis https://hdl.handle.net/1721.1/153898 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Berrones, Antonio Detecting Multimodal Behaviors for Neurodegenerative Disease |
| title | Detecting Multimodal Behaviors for Neurodegenerative Disease |
| title_full | Detecting Multimodal Behaviors for Neurodegenerative Disease |
| title_fullStr | Detecting Multimodal Behaviors for Neurodegenerative Disease |
| title_full_unstemmed | Detecting Multimodal Behaviors for Neurodegenerative Disease |
| title_short | Detecting Multimodal Behaviors for Neurodegenerative Disease |
| title_sort | detecting multimodal behaviors for neurodegenerative disease |
| url | https://hdl.handle.net/1721.1/153898 |
| work_keys_str_mv | AT berronesantonio detectingmultimodalbehaviorsforneurodegenerativedisease |