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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Georgia Tech faculty, staff, and students and any interested members of the public are kindly invited to attend my Ph.D. proposal presentation. Please see the details below.
Date: April 19th, 2021
Time: 9 AM
Location: https://bluejeans.com/371371430
Name: Pradyumna Byappanahalli Suresha
Machine Learning Ph.D. Student
Home Department: School of Electrical and Computer Engineering
Georgia Institute of Technology
Committee
1. Dr. Gari D. Clifford (Advisor) [Chair & Professor, Department of Biomedical Informatics, Emory University School of Medicine; Professor, Department of Biomedical Engineering, Georgia Institute of Technology]
2. Dr. David V. Anderson [Professor, School of Electrical and Computer Engineering, Georgia Institute of Technology]
3. Dr. Omer T. Inan [Associate Professor, School of Electrical and Computer Engineering, Georgia Institute of Technology]
Abstract
Recently, ambient patient monitoring using wearable and nearable sensors is becoming more prevalent, especially in the neurodegenerative (Mild Cognitive Impairment) and neuro-cognitive developmental disorder (Autism and Rett syndrome) populations. Wearables have the advantage of being able to collect high-resolution physiological signal data. However, they suffer from low compliance, and the neurologically impaired populations do not like to wear them or destroy them or forget to wear them. Nearables, on the other hand, do not live on the patient's body and, as a result, have high compliance. In this thesis proposal, we will look at innovative methods for wearable data processing and develop diagnostics using nearables. Finally, we will explore methods to fuse wearable and nearable sensor data to boost the diagnostic powers of the algorithms for classification and prediction of patient state.
