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 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.
Highlights
Diet-related diseases—such as cardiovascular diseases and diabetes—are the leading causes of death globally
Accurate, smartphone-based dietary assessment system that predicts the macronutrient (CHO, PRO, Fat) and calorie content of a meal using two images
The comparison was conducted under the same experimental conditions: The training data was augmented using the same way as [40]; The stochastic gradient descent (SGD) optimizer is applied with the initial learning rate 1 × 10−2 ; The batch size is set as 32 and the number of epochs is 20
Summary
Diet-related diseases—such as cardiovascular diseases and diabetes—are the leading causes of death globally. [24,25] treats the food volume as a latent variable and predicts the food nutrient content directly from the colour image using the CNNs Such approaches achieve ultimate convenience for the end-users, the methods themselves are ill posed and are difficult to generalize in real life. Such approaches require large amount of training data with depth image or food nutrients as ground truth, which is expensive and difficult to acquire To this end, at the current stage, we believe that the two-view geometry-based approach (i.e., the solution of GoCARB) is adequately practical for accurate dietary assessment. Accurate, smartphone-based dietary assessment system that predicts the macronutrient (CHO, PRO, Fat) and calorie content of a meal using two images. We have conducted a study that compares our system’s estimation to the estimations of experienced dietitians, demonstrating the promising advantage of an AI-based system for dietary assessment
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