Capturing children food exposure using wearable cameras and deep learning

Files

2023-04-27_Capturing children food exposure using wearable cameras and deep learning.pdf (4.41 MB)

Date

2023-03-27

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Children’s dietary habits are influenced by complex factors within their home, school and neighborhood environments. Identifying such influencers and assessing their effects is traditionally based on self- reported data which can be prone to recall bias. We developed a culturally acceptable machine-learning-based data-collection system to objectively capture school-children’s exposure to food (including food items, food advertisements, and food outlets) in two urban Arab centers: Greater Beirut, in Lebanon, and Greater Tunis, in Tunisia. Our machine-learning-based system consists of 1) a wearable camera that captures continuous footage of children’s environment during a typical school day, 2) a machine learning model that automatically identifies images related to food from the collected data and discards any other footage, 3) a second machine learning model that classifies food-related images into images that contain actual food items, images that contain food advertisements, and images that contain food outlets, and 4) a third machine learning model that classifies images that contain food items into two classes, corresponding to whether the food items are being consumed by the child wearing the camera or whether they are consumed by others. This manuscript reports on a user-centered design study to assess the acceptability of using wearable cameras to capture food exposure among school children in Greater Beirut and Greater Tunis. We then describe how we trained our first machine learning model to detect food exposure images using data collected from the Web and utilizing the latest trends in deep learning for computer vision. Next, we describe how we trained our other machine learning models to classify food-related images into their respective categories using a combination of public data and data acquired via crowdsourcing. Finally, we describe how the different components of our system were packed together and deployed in a real-world case study and we report on its performance.

Description

Keywords

FOOD IMAGES, NUTRITION, DEEP LEARNING, MACHINE LEARNING, CLASSIFICATION

Citation

URI

http://hdl.handle.net/10625/61983

DOI

Collections

IDRC Research Results / Résultats de recherches du CRDI
2020-2029 / Années 2020-2029
Research Results (CRFS) / Résultats de recherches (SARC)
Research Results (FEH) / Résultats de recherche (AES)