Mehrab Bin Morshed
Ph.D. Candidate, Computer Science
School of Interactive Computing
Georgia Institute of Technology
https://cc.gatech.edu/~mbinmorshed3
Date: Monday, November 28, 2022
Time: 12:00 PM - 3:00 PM (ET)
Physical Location: CODA C1315
Zoom Link: https://gatech.zoom.us/j/94078144698
Committee:
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Dr. Gregory D. Abowd (Advisor), Electrical and Computer Engineering, Northeastern University
Dr. Thomas Plötz (Advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Munmun De Choudhury, School of Interactive Computing, Georgia Institute of Technology
Dr. Andrea G. Parker, School of Interactive Computing, Georgia Institute of Technology
Dr. James M. Rehg, School of Interactive Computing, Georgia Institute of Technology
Dr. Tanzeem Choudhury, Information Science, Cornell Tech
Abstract:
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Eating is one of the most commonly performed activities by humans. The motivation for eating is beyond survival. Eating serves as means for socializing, exploring cultures, etc. Computing researchers have developed various eating detection technologies that can leverage passive sensors available on smart devices to automatically infer when an individual is eating. However, despite their significance in eating literature, crucial contextual information such as meal company, type of food, location of meals, the motivation of eating episodes, etc., are difficult to detect through passive means. My work addresses these challenges by implementing a real-time meal detection system that can trigger questions in the form of EMAs (Ecological Momentary Assessment) to gather crucial contextual information. EMAs are a widely adopted tool used across a variety of disciplines that can gather in-situ details on individual experiences. By leveraging the strength of EMAs to collect eating-related information, my dissertation shows the relationship between various eating contexts and mental well-being for college students and information workers through naturalistic studies.
The contributions of my dissertation are four-fold. First, I developed a real-time meal detection system that can detect meal-level episodes and trigger EMAs to gather contextual data about one’s eating episode. Second, I deploy this system in a college student population to understand their eating behavior during day-to-day life and investigate the relationship of these eating behaviors with various mental well-being outcomes. Third, based on the limitations of passive sensing systems to detect short and sporadic chewing episodes present in snacking, I develop a snacking detection system and operationalize the definition of snacking in this thesis. Finally, I study the causal relationship between stress levels experienced by remote information workers during their workdays and its effect on lunchtime. Finally, this dissertation situates the findings in an interdisciplinary context, including ubiquitous computing, psychology, and nutrition.
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