FIT-EVE&ADAM: Estimation of Velocity & Energy for Automated Diet Activity Monitoring

State-of-the-art techniques for eating activities analysis in dietary monitoring require significant user intervention, which is reported to be one of the major reasons for low adherence. There are limited works using wearables for fine-grained analysis of eating activities in terms of the eating speed, the type of food consumed, and the portion sizes. In this paper, we propose FIT-EVE&ADAM, an armband based diet monitoring system that provides such fine-grained analysis, triggered by a single hand gesture. The system collects the user’s gesture using sensors such as electromyogram embedded in the armband device, along with food image data using color and thermal cameras. Finally, a novel feature selection method is applied on the data features to estimate eating speed and caloric intake with high accuracy (0.96 F1 score). Keywords-Diet Monitoring; Gesture Recognition; Wearable