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Capturing children food exposure using wearable cameras and deep learning.

Shady Elbassuoni1, Hala Ghattas2,3, Jalila El Ati4

  • 1Computer Science Department, American University of Beirut, Beirut, Lebanon.

PLOS Digital Health
|March 27, 2023
PubMed
Summary

This study introduces a novel machine learning system using wearable cameras to objectively track children's food exposure, overcoming limitations of self-reported dietary data. The system accurately identifies and categorizes food-related visuals, offering a more reliable method for understanding environmental influences on children's eating habits.

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Area of Science:

  • Nutrition Science
  • Computer Science
  • Public Health

Background:

  • Children's dietary habits are shaped by multifaceted environmental factors.
  • Traditional data collection methods rely on self-reports, which are susceptible to recall bias.
  • Objective methods are needed to accurately assess children's food exposure in their daily environments.

Purpose of the Study:

  • To develop and evaluate a machine learning-based system for objectively capturing children's food exposure.
  • To assess the cultural acceptability of using wearable cameras for food exposure data collection in urban Arab settings.
  • To train machine learning models for identifying and classifying food-related images from wearable camera footage.

Main Methods:

  • A user-centered design study was conducted to assess the acceptability of wearable cameras among school children in Beirut and Tunis.
  • A machine learning pipeline was developed, including models for food image detection, classification of food-related content (items, ads, outlets), and consumption identification.
  • Models were trained using web data, deep learning techniques, public datasets, and crowdsourced information.

Main Results:

  • The study reports on the user-centered design findings regarding the acceptability of the wearable camera system.
  • The development and training process for the machine learning models designed to detect and classify food exposure are detailed.
  • The system's successful integration and performance in a real-world case study are presented.

Conclusions:

  • The developed machine learning system offers an objective and culturally acceptable method for collecting data on children's food exposure.
  • This technology can provide valuable insights into environmental determinants of children's dietary habits, moving beyond self-reported data.
  • The system's components and their performance in a real-world setting demonstrate its potential for future research and public health interventions.