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

arXiv: https://arxiv.org/abs/2010.07051 · HTML: https://mlsanity.com/paper/2010.07051/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects](https://mlsanity.com/paper/2202.03274/) (2022)
- [Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis](https://mlsanity.com/paper/2312.08592/) (2023)
- [Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring](https://mlsanity.com/paper/2107.00372/) (2021)
- [Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network with Attention](https://mlsanity.com/paper/2211.04253/) (2022)
- [A Closed-Loop Multi-Agent System Driven by LLMs for Meal-Level Personalized Nutrition Management](https://mlsanity.com/paper/2601.04491/) (2026)
- [Robust Multimodal Learning Framework For Intake Gesture Detection Using Contactless Radar and Wearable IMU Sensors](https://mlsanity.com/paper/2507.07261/) (2025)
- [A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors](https://mlsanity.com/paper/2109.03475/) (2021)
- [Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned](https://mlsanity.com/paper/2206.02784/) (2022)
- [Estimation of Food Intake Quantity Using Inertial Signals from Smartwatches](https://mlsanity.com/paper/2502.06649/) (2025)
- [Skeleton-Based Intake Gesture Detection With Spatial-Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2504.10635/) (2025)

## Abstract (arXiv)

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.