I2N: image to nutrients, a sensor guided semi-automated tool for annotation of images for nutrition analysis of eating episodes
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
INTRODUCTION: Dietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user. METHODS: This paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data). RESULTS: In both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N's sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images. DISCUSSION: I2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren't available.
Date Issued
2023-07-27
Date Acceptance
2023-07-05
Citation
Frontiers in Nutrition, 2023, 10, pp.1-9
ISSN
2296-861X
Publisher
Frontiers Media S.A.
Start Page
1
End Page
9
Journal / Book Title
Frontiers in Nutrition
Volume
10
Copyright Statement
Copyright © 2023 Ghosh, McCrory, Marden, Higgins, Anderson, Domfe, Jia, Lo, Frost, Steiner-Asiedu, Baranowski, Sun and Sazonov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/37575335
Subjects
food intake
image annotation
image to nutrients
software
wearable sensor
Publication Status
Published
Coverage Spatial
Switzerland
Article Number
1191962
Date Publish Online
2023-07-27