Smart devices (i.e., smartwatches, smartphones) have enhanced the capability of assessing the well-being of individuals. Researchers in ubiquitous computing, social computing, and human-computer interaction disciplines have demonstrated how activities (e.g, social media activity, physical activity) can serve as indicators for mental and academic well-being of individuals. The data from target individuals were collected from sensors available on commercial smart devices. However, there has been a lack of research on real-time eating detection in relation to the well-being of college students. Most of the past research in the context of eating and well-being gather eating related information using methods (e.g., diary methods, surveys, etc.) that are prone to recall bias. One alternative to such methods is the Experience Sampling Method (ESM). ESM-based questions are short and designed to capture the in-situ experience of an individual within a very short time. In this project, we demonstrate a real-time eating detection system using a commodity smartwatch and propose a research agenda exploiting ESM for capturing the in-situ eating experience for assessing the well-being of Georgia Tech students.
We are interested in ubiquitous computing and the research issues involved in building and evaluating ubicomp applications and services that impact our lives. Much of our work is situated in settings of everyday activity, such as the classroom, the office and the home. Our research focuses on several topics including, automated capture and access to live experiences, context-aware computing, applications and services in the home, natural interaction, software architecture, technology policy, security and privacy issues, and technology for individuals with special needs.
