Eating Behavior In-The-Wild and Its Relationship to Mental Well-Being

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 and, to some extent, what 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, the timing of meals, etc., are difficult to detect through passive means. More importantly, the applications of currently developed automated eating detection systems are limited. My dissertation addresses several of these challenges by combining the strengths of passive sensing technologies and EMAs (Ecological Momentary Assessment). EMAs are a widely adopted tool used across a variety of disciplines that can gather in-situ information about individual experiences. In my dissertation, I demonstrate the relationship between various eating contexts and the mental well-being of college students and information workers through naturalistic studies. The contributions of my dissertation are four-fold. First, I develop 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 investigate the causal relationship between stress levels experienced by remote information workers during their workdays and its effect on lunchtime. This dissertation situates the findings in an interdisciplinary context, including ubiquitous computing, psychology, and nutrition.Ph.D

GVU Center Overview and Funded Research Projects

Presented on August 22, 2019 at 11:30 a.m.-1:00 p.m. in the Technology Square Research Building (TSRB), 1st Floor Ballroom, Georgia Institute of Technology.Keith Edwards is a Professor in the College of Computing at Georgia Tech and Director of the GVU Center. His research interests focus primarily on driving human-centered concerns into core computing infrastructure. He is a pioneer in the exploration of human-centered perspectives on computer networking, particularly in the home and has been active in developing more usable approaches to information security systems. Lately, his research has expanded into a number of explorations of the social impacts of computing technology, and understanding how technology can support the work of non-profits and NGOs. While he is a technologist at heart, he enjoys working with designers, as well as ethnographers and other social scientists.Runtime: 49:23 minutesIn the first GVU Brown Bag Seminar of the academic year, Keith Edwards, GVU Center Director and Professor of Interactive Computing, will kick off our talk series with an overview of the GVU Center detailing its unique resources and opportunities, and previewing some of the events coming up this semester. Come, enjoy lunch, and learn about some of the ways you can connect with GVU. Also, each year, the GVU Center and IPaT announce funding for the Research and Engagement Grants, which support early stage work by Georgia Tech researchers. This year’s winners will give brief overviews of the work they will be doing over the coming academic year

Leveraging WiFi network logs to infer student collocation and its relationship with academic performance

Abstract A comprehensive understanding of collocated social interactions can help campuses and organizations better support their community. Universities could determine new ways to conduct classes and design programs by studying how students have collocated in the past. However, this needs data that describe large groups over a long period. Harnessing user devices to infer collocation, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors across the entire campus is expensive. Instead, we investigate an easily accessible data source that can retroactively depict multiple users on campus over a semester, a managed WiFi network. Despite the coarse approximations of collocation provided by WiFi network logs, we demonstrate that leveraging such data can express meaningful outcomes of collocated social interaction. Since a known outcome of collocating with peers is improved performance, we inspected if automatically–inferred collocation behaviors can indicate the individual performance of project group members on a campus. We studied 163 students (in 54 project groups) over 14 weeks. After describing how we determine collocation with the WiFi logs, we present a study to analyze how collocation within groups relates to a student’s final score. We found that modeling collocation behaviors showed a significant correlation (Pearson’s r = 0.24 $r =0.24$ ) with performance (better than models of peer feedback or individual behaviors). These findings emphasize that it is feasible and valuable to characterize collocated social interactions with archived WiFi network logs. We conclude the paper with a discussion of applications for repurposing WiFi logs to describe collocation, along with privacy considerations, and directions for future work