GOCARB

Type 1 Diabetes Self-Management and Carbohydrate Counting: A Computer Vision based Approach

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 Obiettivo del progetto (Objective)

'Patients with diabetes must be taught how to achieve glycaemic control by monitoring their glucose levels properly. They are medicated with either exogenous insulin or other drugs and encouraged to improve their diet and physical activity. Although studies have shown that planning meals and counting carbohydrates is of great importance for diabetic patients, even well trained diabetic patients find it difficult to estimate carbohydrates precisely.

The aim of the project is the design, development and evaluation of a system which will permit the automatic, near real-time recognition of the different types of foods on a plate and the estimation of their content of carbohydrates. The system will be based on the advanced analysis of colour images and will be composed of i) an Advanced Image Processing (AIP) module, including a camera for image capture, ii) a Carbohydrate Estimator (CE) and iii) a Data Base (DB). The AIP module will incorporate the entire image processing tools for acquisition, preprocessing, segmentation, feature detection, feature representation and selection, and classification. The CE will estimate the volume and the weight of the food, while the DB will contain a list of nutrients, along with the corresponding grams of carbohydrates. In a typical use scenario, the diabetic will take a picture of the incoming meal with the mobile phone camera. This image will be processed in order to estimate a set of characteristic features describing the type of nutrition and the corresponding grams of carbohydrate. In addition to dietary assessment, this information will be used to optimise the calculation of the bolus insulin dose.

The ultimate objective is to have an application running on a portable device which can be used in everyday life to support the diabetic patient during carbohydrate counting and insulin dose estimation in a precise, easy and flexible manner.'

Introduzione (Teaser)

The importance of planning meals and counting carbohydrates is of undisputed importance for diabetic patients. A transatlantic study is making this possible through a revolutionary system capable of precisely counting the carbs in each meal.

Descrizione progetto (Article)

The increasing prevalence of diabetes in modern societies calls for intervention to alleviate disease symptoms and improve the quality of life of sufferers. However, counting carbohydrates in each meal is not always an easy task.

The EU-funded 'Type 1 diabetes self-management and carbohydrate counting: a computer vision based approach' (http://www.gocarb.eu/ (GOCARB)) project is an academia-industry alliance formed to address the needs of diabetics. In this context, partners have set out to design an automated system for effective carbohydrate counting.

The whole concept is based on the idea of taking a photograph of your food with a mobile phone. With the aid of appropriate software the different food items on the plate are segmented, recognised and reconstructed. Using a reference object, the volume of each item is estimated and the carbohydrate content calculated using nutritional databases. The information is ultimately utilised for estimating the optimal prandial insulin dose required in each case.

So far, over 5 000 food images have been compiled in a visual dataset and the appropriate algorithms have been designed for food recognition and volume estimation. The computational tool for estimating the grams of carbohydrates contained in a meal by accessing nutritional databases is currently being developed. Additionally, researchers are in the process of finalising the prototype for estimating the needed prandial insulin dose given the carbohydrate content of the recent meal.

The GOCARB tool has been patented and disseminated to the diabetes technology and biomedical engineering communities. Partners are hopeful that clinical evaluation of the GOCARB smartphone application will prove its validity as a means for diabetes self-management.