A Smart Utensil for Detecting Food Pick-Up Gesture and Amount While Eating
Higher food intake rates (i.e., eating too fast) are linked to several health concerns such as an elevated risk of obesity or gastritis. Raising awareness of one’s eating habits can regulate one’s pace of food intake. In this paper, we propose a novel smart-eating utensil that can potentially increase the users’ awareness of their eating rate by detecting their food pick-up gesture as well as the weight upon each bite. We design and implement a proof-of-concept prototype fork with multiple embedded sensors and processor to collect the eating data. After that, we propose a solution for food pick-up gesture detection and food amount estimation in each food pick-up. We assess the accuracy of our solution through ten successful data collection sessions with participants.We demonstrate that our method has strong potential to accurately detect the food pick-up gesture and estimate the amount of food on each pick-up.
Zuoyi Zhang, Huizhe Zheng, Sawyer Rempel, Kenny Hong, Teng Han, Yumiko Sakamoto, and Pourang Irani. 2020. A smart utensil for detecting food pick-up gesture and amount while eating. In Proceedings of the 11th Augmented Human International Conference (AH '20). Association for Computing Machinery, New York, NY, USA, Article 2, 1–8. DOI:https://doi.org/10.1145/3396339.3396361 (3rd best paper award)
Canada Research Chair
at University of British Columbia Okanagan Campus