Vasiloglou, Maria F.; Lu, Ya; Stathopoulou, Thomai; Papathanail, Ioannis; Faeh, David; Ghosh, Arindam; Baumann, Manuel; Mougiakakou, Stavroula (2020). Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project. Nutrients, 12(12) Molecular Diversity Preservation International MDPI 10.3390/nu12123763
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The Mediterranean diet (MD) is regarded as a healthy eating pattern with beneficial effects both for the decrease of the risk for non-communicable diseases and also for body weight reduction. In the current manuscript, we propose an automated smartphone application which monitors and evaluates the user's adherence to MD using images of the food and drinks that they consume. We define a set of rules for automatic adherence estimation, which focuses on the main MD food groups. We use a combination of a convolutional neural network (CNN) and a graph convolutional network to detect the types of foods and quantities from the users' food images and the defined set of rules to evaluate the adherence to MD. Our experiments show that our system outperforms a basic CNN in terms of recognizing food items and estimating quantity and yields comparable results as experienced dietitians when it comes to overall MD adherence estimation. As the system is novel, these results are promising; however, there is room for improvement of the accuracy by gathering and training with more data and certain refinements can be performed such as re-defining the set of rules to also be able to be used for sub-groups of MD (e.g., vegetarian type of MD).
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition |
Graduate School: |
Graduate School for Cellular and Biomedical Sciences (GCB) |
UniBE Contributor: |
Vasiloglou, Maria, Lu, Ya, Stathopoulou, Thomai, Papathanail, Ioannis, Mougiakakou, Stavroula |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health 600 Technology > 620 Engineering |
ISSN: |
2072-6643 |
Publisher: |
Molecular Diversity Preservation International MDPI |
Funders: |
[UNSPECIFIED] Innosuisse |
Submitter: |
Stavroula Mougiakakou |
Date Deposited: |
09 Mar 2021 11:04 |
Last Modified: |
05 Dec 2022 15:48 |
Publisher DOI: |
10.3390/nu12123763 |
PubMed ID: |
33297550 |
BORIS DOI: |
10.48350/152852 |
URI: |
https://boris.unibe.ch/id/eprint/152852 |
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