Deep Learning-empowered Mobile System for Daily Healthcare: A Survey

PhD Qualifying Examination


Title: "Deep Learning-empowered Mobile System for Daily Healthcare: A 
Survey"

by

Mr. Baichen YANG


Abstract:

Daily healthcare is one of the key research directions toward the 
next-generation healthcare system, where individuals can easily monitor 
their health conditions and access personalized medical advice in daily 
scenarios. Recent years have witnessed a rapid development of the Internet 
of Things (IoT) technology, whose ubiquitous sensing abilities have made 
mobile systems perceive more and more information. In the meantime, deep 
learning has shown its superiority in data analysis in several fields, 
including natural language processing and computer vision. Integrating 
deep learning methods and mobile systems begins to enable a series of new 
applications for daily healthcare, making it more accessible and 
personalized.

In this survey, we summarize recent mobile systems for daily healthcare 
applications with a special focus on the deep learning algorithms 
utilized. We first give a general review of the basic concepts of mobile 
systems and deep learning, and mainly discuss recent daily healthcare 
applications with deep learning-empowered mobile systems. We categorize 
the applications into three classes, including behavior monitoring, vital 
sign monitoring as well as disease assessment. We also introduce our work 
on daily Parkinson’s disease assessment as our first attempt toward 
designing mobile systems for daily healthcare. At the end of this survey, 
we discuss future research directions and conclude this survey.


Date:                   Tuesday, 25 April 2023

Time:                   3:00pm - 5:00pm

Venue:                  Room 5501
                         Lifts 25/26

Committee Members:      Prof. Qian Zhang (Supervisor)
                         Prof. Gary Chan (Chairperson)
 			Prof. Bo Li
 			Dr. Shuai Wang


**** ALL are Welcome ****