Feasibility of Identifying Eating Moments from First-Person Images Leveraging Human Computation

There is widespread agreement in the medical research com-munity that more effective mechanisms for dietary assess-ment and food journaling are needed to fight back against obesity and other nutrition-related diseases. However, it is presently not possible to automatically capture and objec-tively assess an individual’s eating behavior. Currently used dietary assessment and journaling approaches have several limitations; they pose a significant burden on individuals and are often not detailed or accurate enough. In this paper, we describe an approach where we leverage human computation to identify eating moments in first-person point-of-view im-ages taken with wearable cameras. Recognizing eating mo-ments is a key first step both in terms of automating dietary assessment and building systems that help individuals reflect on their diet. In a feasibility study with 5 participants over 3 days, where 17,575 images were collected in total, our method was able to recognize eating moments with 89.68% accuracy