A food allergy risk detection model based on situation awareness

Abstract

Advances in ubiquitous computing are enabling the emergence of opportunities in many areas; among them we highlight the health-related applications. In this area, there is a trend toward developing many applications that enable care wherever you are and whenever you need, called ubiquitous healthcare. A detailed survey of existing and proposed models has shown that none of these applications meets the needs of people who suffer from food allergy. Thus, this article proposes an allergy detection ubiquitous model based on situation awareness. This model, called Allergy Detector, focuses on food allergy, in particular the eight major allergens (peanut, milk, egg, wheat, soy, fish, crustacean and tree nuts) and their derivatives, which cause about 90% of all food allergies. The main scientific contribution of the Allergy Detector is the use of situation awareness for detecting allergies anytime and anywhere in a transparent manner. In order to assess the model, we designed some case studies. We also carried out a profiling, accessing the Allergy Detector in terms of performance and identifying the main bottlenecks. The results showed the feasibility of using the model for detecting allergy ubiquitously.

Keywords: ubiquitous computing, context awareness, ubiquituos health.