🏅 Honorable Mention Award
Various automated eating detection wearables have been developed to monitor food intake, addressing the limitations of manual journaling. However, many existing systems lack accuracy in real-world settings or have intrusive designs like headgear. Eyeglasses present a socially acceptable alternative, but current solutions require custom frames and high power consumption. We introduce MyDJ, an eating detection system attachable to any eyeglass frame, which combines a piezoelectric sensor and an accelerometer to capture chewing signals accurately and efficiently. Tested with 30 subjects in uncontrolled environments, including a week-long trial with six users wearing MyDJ on their own glasses, the system achieved a 91.9% F1-score in detecting eating episodes and offered 5.1 times longer battery life compared to existing technologies. Participants also found MyDJ nearly as comfortable (94.8%) as regular eyeglasses.
Jaemin Shin, Seungjoo Lee, Taesik Gong, Hyungjun Yoon, Hyunchul Roh, Andrea Bianchi, and Sung-Ju Lee. 2022. MyDJ: Sensing Food Intakes with an Attachable on Your Eyeglass Frame. In CHI Conference on Human Factors in Computing Systems (CHI ‘22). Association for Computing Machinery, New York, NY, USA, Article 341, 1–17. DOI: https://doi.org/10.1145/3491102.3502041