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Article

goFOODTM: An Artificial Intelligence System for Dietary Assessment

1
ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland
2
Division of Endocrinology, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA
3
Luminis Health, Anne Arundel Medical Center, Anne Arundel Medical Group Diabetes and Endocrine Specialists, Annapolis, MD 21401, USA
4
Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
5
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; https://doi.org/10.3390/s20154283
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. https://doi.org/10.3390/s20154283

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. https://doi.org/10.3390/s20154283

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. https://doi.org/10.3390/s20154283

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