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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
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Capturing Eating Behavior from Video Analysis: A Systematic Review.

Michele Tufano1, Marlou Lasschuijt1, Aneesh Chauhan2

  • 1Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands.

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|November 26, 2022
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Summary
This summary is machine-generated.

Automated methods for detecting eating events like bites and chews from videos show promise. Facial landmarks offer the best accuracy for counting bites and chews, paving the way for healthier eating interventions.

Keywords:
AIautomatic analysiscomputer visioneating behaviorhealthy eating

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Nutritional Science

Background:

  • Current methods for detecting eating behavior events (bites, chews, swallows) lack objectivity, standardization, and automation.
  • Video recordings offer a non-invasive, scalable data source for automated analysis of eating episodes.

Approach:

  • A systematic review following PRISMA guidelines screened 277 publications (2010-2021) from major databases.
  • 13 publications were included for analysis, categorizing methods and evaluating their accuracy for automated eating behavior detection.

Key Points:

  • Facial landmarks achieve high accuracy for bite (90%) and chew (60%) detection, with lower accuracy for food liking (25%).
  • Deep neural networks excel at bite (91%) and gesture intake (86%) detection.
  • Active appearance models (93% chew detection) and optical flow (88% chew detection) are also effective for chew analysis.

Conclusions:

  • Facial landmarks are currently optimal for automated bite and chew counting, though improvements are needed.
  • Future research should focus on accurate, low-cost, and computationally efficient methods for detecting bites, chews, and swallows.
  • Automatic eating behavior analysis can facilitate research and enable real-time interventions for promoting healthy eating habits.