This paper is in the following e-collection/theme issue:

mHealth for Data Collection and Research (917) Innovations and Technology for Healthy Eating Education (400) Instruments and Questionnaires for Nutrition and Food Intake (159) mHealth for Wellness, Behavior Change and Prevention (2754)

Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15294, first published .
Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study

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

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

Authors of this article:

David Herzig1 Author Orcid Image ;   Christos T Nakas2, 3 Author Orcid Image ;   Janine Stalder1 Author Orcid Image ;   Christophe Kosinski1 Author Orcid Image ;   Céline Laesser1 Author Orcid Image ;   Joachim Dehais1 Author Orcid Image ;   Raphael Jaeggi1 Author Orcid Image ;   Alexander Benedikt Leichtle3 Author Orcid Image ;   Fried-Michael Dahlweid4 Author Orcid Image ;   Christoph Stettler1 Author Orcid Image ;   Lia Bally1 Author Orcid Image
Article Authors Cited by (18) Tweetations (7) Metrics

Journals

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  8. Moshfegh A, Rhodes D, Martin C. National Food Intake Assessment: Technologies to Advance Traditional Methods. Annual Review of Nutrition 2022;42(1):401 View
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  10. Zhang S, Callaghan V, Che Y. Image-based methods for dietary assessment: a survey. Journal of Food Measurement and Characterization 2024;18(1):727 View
  11. Serra M, Alceste D, Hauser F, Hulshof P, Meijer H, Thalheimer A, Steinert R, Gerber P, Spector A, Gero D, Bueter M. Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water. Frontiers in Nutrition 2023;10 View
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Books/Policy Documents

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