goFOOD[TM]: An Artificial Intelligence System for Dietary Assessment

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

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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 and 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:

10 Aug 2020 14:14

Publisher DOI:

https://doi.org/10.3390/s20154283

BORIS DOI:

10.7892/boris.145662

URI:

https://boris.unibe.ch/id/eprint/145662

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