  The increased worldwide prevalence of obesity has sparked the interest of the
scientific community towards tools that objectively and automatically monitor
eating behavior. Despite the study of obesity being in the spotlight, such
tools can also be used to study eating disorders (e.g. anorexia nervosa) or
provide a personalized monitoring platform for patients or athletes. This paper
presents a complete framework towards the automated i) modeling of in-meal
eating behavior and ii) temporal localization of meals, from raw inertial data
collected in-the-wild using commercially available smartwatches. Initially, we
present an end-to-end Neural Network which detects food intake events (i.e.
bites). The proposed network uses both convolutional and recurrent layers that
are trained simultaneously. Subsequently, we show how the distribution of the
detected bites throughout the day can be used to estimate the start and end
points of meals, using signal processing algorithms. We perform extensive
evaluation on each framework part individually. Leave-one-subject-out (LOSO)
evaluation shows that our bite detection approach outperforms four
state-of-the-art algorithms towards the detection of bites during the course of
a meal (0.923 F1 score). Furthermore, LOSO and held-out set experiments
regarding the estimation of meal start/end points reveal that the proposed
approach outperforms a relevant approach found in the literature (Jaccard Index
of 0.820 and 0.821 for the LOSO and heldout experiments, respectively).
Experiments are performed using our publicly available FIC and the newly
introduced FreeFIC datasets.
