Title: A study on the usage of wearable and nearable sensors for classification and prediction of patient state

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.