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goFOODTM: An Artificial Intelligence System for Dietary Assessment

ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland
Division of Endocrinology, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA
Luminis Health, Anne Arundel Medical Center, Anne Arundel Medical Group Diabetes and Endocrine Specialists, Annapolis, MD 21401, USA
Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
Bern University Hospital “Inselpital”, 3010 Bern, Switzerland
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(15), 4283;
Received: 19 May 2020 / Revised: 29 July 2020 / Accepted: 29 July 2020 / Published: 31 July 2020
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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. View Full-Text
Keywords: carbohydrate; protein; fat; calorie; nutrient estimation; computer vision; smartphone carbohydrate; protein; fat; calorie; nutrient estimation; computer vision; smartphone
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MDPI and ACS Style

Lu, Y.; Stathopoulou, T.; Vasiloglou, M.F.; Pinault, L.F.; Kiley, C.; Spanakis, E.K.; Mougiakakou, S. goFOODTM: An Artificial Intelligence System for Dietary Assessment. Sensors 2020, 20, 4283.

AMA Style

Lu Y, Stathopoulou T, Vasiloglou MF, Pinault LF, Kiley C, Spanakis EK, Mougiakakou S. goFOODTM: An Artificial Intelligence System for Dietary Assessment. Sensors. 2020; 20(15):4283.

Chicago/Turabian Style

Lu, Ya, Thomai Stathopoulou, Maria F. Vasiloglou, Lillian F. Pinault, Colleen Kiley, Elias K. Spanakis, and Stavroula Mougiakakou. 2020. "goFOODTM: An Artificial Intelligence System for Dietary Assessment" Sensors 20, no. 15: 4283.

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