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    goFOOD<TM>: An artificial intelligence system for dietary assessment

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    Author
    Lu, Ya
    Stathopoulou, Thomai
    Vasiloglou, Maria F.
    Pinault, Lillian F.
    Kiley, Colleen
    Spanakis, Elias K.
    Mougiakakou, Stavroula
    Date
    2020-08-01
    Journal
    Sensors
    Publisher
    MDPI AG
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3390/s20154283
    Abstract
    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 goFOOD™. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOOD™ 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. goFOOD™ 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 goFOOD™ performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOOD™ provides a simple and efficient solution to the end-user for dietary assessment. © 2020 by the authors.
    Keyword
    Calorie
    Carbohydrate
    Computer vision
    Fat
    Nutrient estimation
    Protein
    Smartphone
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/13540
    ae974a485f413a2113503eed53cd6c53
    10.3390/s20154283
    Scopus Count
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