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Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study.

David Herzig1, Christos T Nakas2,3, Janine Stalder1

  • 1Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital, University of Bern, Bern, Switzerland.

JMIR Mhealth and Uhealth
|March 27, 2020
PubMed
Summary

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A new smartphone app accurately quantifies meal macronutrient content using depth-sensing technology and computer vision. This dietary intake tool offers a user-friendly and precise method for tracking nutrition, aiding metabolic disorder management.

Area of Science:

  • Nutrition Science
  • Biomedical Engineering
  • Computer Vision

Background:

  • Accurate dietary intake quantification is crucial for managing metabolic disorders.
  • Traditional methods for dietary assessment are often inaccurate and labor-intensive.
  • Advancements in smartphone depth-sensing and computer vision offer potential for improved food intake quantification.

Purpose of the Study:

  • To evaluate the accuracy of a novel smartphone application that uses depth-sensing hardware and computer vision for quantifying the macronutrient content of meals via volumetry.
  • To assess the system's performance in estimating meal weight, macronutrient composition (carbohydrates, protein, fat), and energy content.

Main Methods:

  • The study utilized a smartphone with a structured light depth sensor (iPhone X).
Keywords:
computer visiondepth cameradietary assessmentsmartphone

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  • A novel app estimated the weight, macronutrient, and energy content of 48 diverse meals (128 food items).
  • Reference weights were obtained using a precision scale; endpoints included estimation error, segmentation performance, and processing time.

    • Main Results:

    • The app demonstrated mean absolute errors of 14.0% for weight, 14.8% for carbohydrates, 12.3% for fat, 13.0% for protein, and 12.7% for energy.
    • Estimation accuracy was consistent across viewing angles but varied slightly by meal type (cooked meals performed slightly worse).
    • Segmentation adjustment was needed for a small fraction of items (7/128), with a mean processing time of 22.9 seconds.

    Conclusions:

    • The novel smartphone app provides highly accurate volume estimation for a wide variety of food items.
    • The system exhibits excellent segmentation performance and minimal processing time, indicating high usability.
    • This technology presents a promising tool for precise dietary intake assessment and management of metabolic health.