Lu, Ya; Stathopoulou, Thomai; Vasiloglou, Maria F.; Pinault, Lillian; Kiley, Colleen; Spanakis, Elias; Mougiakakou, Stavroula (2020). goFOOD[TM]: An Artificial Intelligence System for Dietary Assessment. Sensors, 20(4283) Molecular Diversity Preservation International MDPI https://doi.org/10.3390/s20154283
|
Text
sensors-20-04283-v2-1.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (13MB) | Preview |
Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.
Item Type: |
Journal Article (Original Article) |
|---|---|
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: |
Lu, Ya, Stathopoulou, Thomai, Vasiloglou, Maria, Mougiakakou, Stavroula |
Subjects: |
600 Technology > 610 Medicine & health 600 Technology > 620 Engineering |
ISSN: |
1424-8220 |
Publisher: |
Molecular Diversity Preservation International MDPI |
Language: |
English |
Submitter: |
Stavroula Mougiakakou |
Date Deposited: |
10 Aug 2020 14:06 |
Last Modified: |
05 Dec 2022 15:39 |
Publisher DOI: |
https://doi.org/10.3390/s20154283 |
BORIS DOI: |
10.7892/boris.145662 |
URI: |
https://boris.unibe.ch/id/eprint/145662 |
Actions (login required)
 |
Edit item |