Capturing children food exposure using wearable cameras and deep learning

Shady Elbassuoni*, Hala Ghattas, Jalila El Ati, Yorgo Zoughby, Aline Semaan, Christelle Akl, Tarek Trabelsi, Reem Talhouk, Houda Ben Gharbia, Zoulfikar Shmayssani, Aya Mourad, SCALE Research Group

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article numbere0000211
Number of pages22
JournalPLOS Digital Health
Volume2
Issue number3
DOIs
Publication statusPublished - 27 Mar 2023

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