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Capturing children food exposure using wearable cameras and deep learning
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-l...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042366/ https://www.ncbi.nlm.nih.gov/pubmed/36972212 http://dx.doi.org/10.1371/journal.pdig.0000211 |
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author | Elbassuoni, Shady Ghattas, Hala El Ati, Jalila Zoughby, Yorgo Semaan, Aline Akl, Christelle Trabelsi, Tarek Talhouk, Reem Ben Gharbia, Houda Shmayssani, Zoulfikar Mourad, Aya |
author_facet | Elbassuoni, Shady Ghattas, Hala El Ati, Jalila Zoughby, Yorgo Semaan, Aline Akl, Christelle Trabelsi, Tarek Talhouk, Reem Ben Gharbia, Houda Shmayssani, Zoulfikar Mourad, Aya |
author_sort | Elbassuoni, Shady |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10042366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100423662023-03-28 Capturing children food exposure using wearable cameras and deep learning Elbassuoni, Shady Ghattas, Hala El Ati, Jalila Zoughby, Yorgo Semaan, Aline Akl, Christelle Trabelsi, Tarek Talhouk, Reem Ben Gharbia, Houda Shmayssani, Zoulfikar Mourad, Aya PLOS Digit Health Research Article 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. Public Library of Science 2023-03-27 /pmc/articles/PMC10042366/ /pubmed/36972212 http://dx.doi.org/10.1371/journal.pdig.0000211 Text en © 2023 Elbassuoni et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Elbassuoni, Shady Ghattas, Hala El Ati, Jalila Zoughby, Yorgo Semaan, Aline Akl, Christelle Trabelsi, Tarek Talhouk, Reem Ben Gharbia, Houda Shmayssani, Zoulfikar Mourad, Aya Capturing children food exposure using wearable cameras and deep learning |
title | Capturing children food exposure using wearable cameras and deep learning |
title_full | Capturing children food exposure using wearable cameras and deep learning |
title_fullStr | Capturing children food exposure using wearable cameras and deep learning |
title_full_unstemmed | Capturing children food exposure using wearable cameras and deep learning |
title_short | Capturing children food exposure using wearable cameras and deep learning |
title_sort | capturing children food exposure using wearable cameras and deep learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042366/ https://www.ncbi.nlm.nih.gov/pubmed/36972212 http://dx.doi.org/10.1371/journal.pdig.0000211 |
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