A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches

TL;DR

This paper introduces an end-to-end neural network framework for automatically detecting eating events and localizing meals in real-world settings using smartwatch inertial data, with high accuracy demonstrated on public datasets.

Contribution

The study presents a novel neural network architecture combining convolutional and recurrent layers for bite detection and a signal processing method for meal segmentation, outperforming existing approaches.

Findings

Bite detection F1 score of 0.923 surpasses state-of-the-art methods.

Meal start/end estimation achieved Jaccard Index over 0.82.

Framework validated on publicly available datasets.

Abstract

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.