Dataset Information

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

ABSTRACT:

Background

Quantification of dietary intake is key to the prevention and management of numerous metabolic disorders. Conventional approaches are challenging, laborious, and lack accuracy. The recent advent of depth-sensing smartphones in conjunction with computer vision could facilitate reliable quantification of food intake.

Objective

The objective of this study was to evaluate the accuracy of a novel smartphone app combining depth-sensing hardware with computer vision to quantify meal macronutrient content using volumetry.

Methods

The app ran on a smartphone with a built-in depth sensor applying structured light (iPhone X). The app estimated weight, macronutrient (carbohydrate, protein, fat), and energy content of 48 randomly chosen meals (breakfasts, cooked meals, snacks) encompassing 128 food items. The reference weight was generated by weighing individual food items using a precision scale. The study endpoints were (1) error of estimated meal weight, (2) error of estimated meal macronutrient content and energy content, (3) segmentation performance, and (4) processing time.

Results

In both absolute and relative terms, the mean (SD) absolute errors of the app's estimates were 35.1 g (42.8 g; relative absolute error: 14.0% [12.2%]) for weight; 5.5 g (5.1 g; relative absolute error: 14.8% [10.9%]) for carbohydrate content; 1.3 g (1.7 g; relative absolute error: 12.3% [12.8%]) for fat content; 2.4 g (5.6 g; relative absolute error: 13.0% [13.8%]) for protein content; and 41.2 kcal (42.5 kcal; relative absolute error: 12.7% [10.8%]) for energy content. Although estimation accuracy was not affected by the viewing angle, the type of meal mattered, with slightly worse performance for cooked meals than for breakfasts and snacks. Segmentation adjustment was required for 7 of the 128 items. Mean (SD) processing time across all meals was 22.9 seconds (8.6 seconds).

Conclusions

This study evaluated the accuracy of a novel smartphone app with an integrated depth-sensing camera and found highly accurate volume estimation across a broad range of food items. In addition, the system demonstrated high segmentation performance and low processing time, highlighting its usability.

SUBMITTER: Herzig D

PROVIDER: S-EPMC7142738 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Herzig David D Nakas Christos T CT Stalder Janine J Kosinski Christophe C Laesser Céline C Dehais Joachim J Jaeggi Raphael R Leichtle Alexander Benedikt AB Dahlweid Fried-Michael FM Stettler Christoph C Bally Lia L

JMIR mHealth and uHealth 20200325 3

<h4>Background</h4>Quantification of dietary intake is key to the prevention and management of numerous metabolic disorders. Conventional approaches are challenging, laborious, and lack accuracy. The recent advent of depth-sensing smartphones in conjunction with computer vision could facilitate reliable quantification of food intake.<h4>Objective</h4>The objective of this study was to evaluate the accuracy of a novel smartphone app combining depth-sensing hardware with computer vision to quantif ...[more]

PMID: 32209531

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Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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