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Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review.

Xin Chen1, Ernest N Kamavuako1,2

  • 1Department of Engineering, King's College London, London WC2R 2LS, UK.

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Summary
This summary is machine-generated.

Monitoring food and fluid intake is crucial for health. This review examines vision-based methods for intake monitoring, highlighting their potential and challenges in recognizing intake actions and estimating volumes.

Keywords:
dietary monitoringdrinking action detectionintake monitoringvision-based methods

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Nutritional Science

Background:

  • Accurate food and fluid intake monitoring is vital for preventing dehydration, malnutrition, and obesity.
  • Current research predominantly focuses on dietary monitoring, often neglecting fluid intake.
  • Vision-based methods offer a non-intrusive approach to monitoring food and fluid consumption.

Purpose of the Study:

  • To comprehensively review existing literature on vision-based food and fluid intake monitoring.
  • To assess the scope and identify research gaps in this domain.
  • To analyze patterns in device selection, algorithms, and experimental settings.

Main Methods:

  • Systematic literature review of 253 articles on vision-based intake monitoring.
  • Analysis of data on device types, viewing angles, and monitoring tasks.
  • Evaluation of algorithms, experimental setups, and reported performance metrics.

Main Results:

  • Vision-based methods show promise in food/beverage recognition, action detection, and volume estimation.
  • Significant challenges remain, including occlusion, privacy concerns, and computational efficiency.
  • The review identifies trends in sensor technology, algorithms, and performance evaluation.

Conclusions:

  • Vision-based intake monitoring is a rapidly developing field with substantial potential.
  • Addressing current challenges is key to improving the practicality and widespread adoption of these systems.
  • Further research is needed to overcome limitations and enhance the accuracy and robustness of vision-based monitoring solutions.