FoodScrap: Promoting Rich Data Capture and Reflective Food Journaling Through Speech Input
Yuhan Luo, Young-Ho Kim, Bongshin Lee, Naeemul Hassan, Eun Kyoung Choe
Abstract
The factors influencing people’s food decisions, such as one’s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a week-long data collection study (N = 11). We deployed FoodScrap, a speech-based food journaling app that allows people to capture food components, preparation methods, and food decisions. Using speech input, participants detailed their meal ingredients and elaborated their food decisions by describing the eating moments, explaining their eating strategy, and assessing their food practice. Participants recognized that speech input facilitated self-reflection, but expressed concerns around re-recording, mental load, social constraints, and privacy. We discuss how speech input can support low-burden and reflective food journaling and opportunities for effectively processing and presenting large amounts of speech data.