goFOOD^TM : An Artificial Intelligence System for Dietary Assessment

• Main Authors: Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou, Lillian F. Pinault, Colleen Kiley, Elias K. Spanakis, Stavroula Mougiakakou
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Sensors
• Subjects: carbohydrate; protein; fat; calorie; nutrient estimation; computer vision
• Online Access: https://www.mdpi.com/1424-8220/20/15/4283

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^TM . The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOOD^TM 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^TM 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^TM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOOD^TM provides a simple and efficient solution to the end-user for dietary assessment.