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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 11th Augmented Human International Conference (AH ’20), May 27–29, 2020, Winnipeg, MB, Canada. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3396339.3396361

3rd  best paper award

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Mindful Eating

Authors

Zuoyi Zhang

Zuoyi Zhang

MSc Student
Leo Zheng

Leo Zheng

Undergraduate Student
Sawyer Rempel

Sawyer Rempel

Undergraduate Student
Kenny Hong

Kenny Hong

Alumni
Teng Han

Teng Han

Alumni
Dr. Yumiko Sakamoto

Dr. Yumiko Sakamoto

Research Associate
Pourang Irani

Pourang Irani

Professor
Canada Research Chair