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Related Concept Videos

Binge Eating Disorders01:23

Binge Eating Disorders

Binge eating disorder is a significant mental health condition characterized by recurrent episodes of excessive food consumption within a short period, accompanied by a perceived loss of control over eating behavior. Unlike occasional overeating, binge eating disorder is marked by distressing emotions such as guilt, shame, and anxiety following binge episodes. The disorder affects individuals across different ages and backgrounds, with profound implications for physical and psychological...

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Related Experiment Video

Updated: Jun 12, 2026

Control of Eating Behavior Using a Novel Feedback System
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A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.

Delwar Hossain1, J Graham Thomas2,3, Megan A McCrory4

  • 1Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35401, USA.

Sensors (Basel, Switzerland)
|May 28, 2025
PubMed
Summary
This summary is machine-generated.

Sensor technology can measure eating behavior, from chewing to environment. This review details sensor types, accuracy, and future needs for real-world, privacy-preserving eating monitoring.

Keywords:
dietary intakeeating behaviormeal microstructuresensortechnologywearable sensor

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Nutritional Science

Background:

  • Eating behavior is a complex dynamic process involving multiple factors like chewing, swallowing, food type, and environmental context.
  • Understanding and quantifying eating behavior is crucial for various applications, including health monitoring, dietary assessment, and personalized nutrition.

Purpose of the Study:

  • To systematically review the application of sensor technology in measuring and monitoring eating behavior.
  • To establish a taxonomy of sensors used for quantifying diverse eating metrics.
  • To assess the accuracy of sensor-based measurement devices and methods.

Main Methods:

  • A systematic review following PRISMA 2020 guidelines.
  • Analysis of 161 scientific manuscripts focusing on sensor technology for eating behavior.
  • Classification of sensors into categories such as acoustic, motion, strain, distance, physiological, and cameras.

Main Results:

  • A comprehensive taxonomy of sensors for quantifying eating behavior was established.
  • The accuracy of various measurement devices and methods was assessed, highlighting advantages and limitations.
  • The review identified the potential of combining sensor modalities with machine learning algorithms for comprehensive eating behavior analysis.

Conclusions:

  • Sensor technology offers promising avenues for objective measurement of eating behavior.
  • Further research is needed to validate methods in real-world settings and develop privacy-preserving techniques.
  • Future trends include integrating multiple sensor modalities and advanced algorithms for robust and user-centric eating monitoring systems.