🏅 Honorable mention award
Various automated eating detection wearables have been proposed to monitor food intakes. While these systems overcome the forgetfulness of manual user journaling, they typically show low accuracy at outside-the-lab environments or have intrusive form-factors (e.g., headgear). Eyeglasses are emerging as a socially-acceptable eating detection wearable, but existing approaches require custom-built frames and consume large power. We propose MyDJ, an eating detection system that could be attached to any eyeglass frame. MyDJ achieves accurate and energy-efficient eating detection by capturing complementary chewing signals on a piezoelectric sensor and an accelerometer. We evaluated the accuracy and wearability of MyDJ with 30 subjects in uncontrolled environments, where six subjects attached MyDJ on their own eyeglasses for a week. Our study shows that MyDJ achieves 91.9% F1-score in eating episode coverage, with 5.1× battery time over the state-of-the-art systems. In addition, participants reported wearing MyDJ was almost as comfortable (94.8%) as wearing regular eyeglasses.
Jaemin Shin, Seungjoo Lee, Taesik Gong, Hyungjun Yoon, Hyunchul Roh, Andrea Bianchi, Sung-Ju Lee. 2022. MyDJ: Sensing Food Intakes with an Attachable on Your Eyeglass Frame. To appear in proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI’22)