A hypothetical reasoning system for mobile health and wellness applications

In the last years, rule-based systems have been more and more used in mobile health and wellness applications for embedding and reasoning over domain-specific knowledge and suggesting actions to perform. However, often, no sufficient information is available to infer definite indications about the action to perform and one or more hypothesis should be formulated and evaluated with respect to their possible impacts. In order to face this issue, this paper proposes a mobile hypothetical reasoning system able to evaluate set of hypotheses, infer their outcomes and support the user in choosing the best one. In particular, it offers facilities to: i) build specific scenarios starting from different initial hypothesis formulated by the user; ii) optimize them by eliminating common domain-specific elements and avoiding their processing more than once; ii) efficiently evaluate a set of logic rules over the optimized scenarios directly on the mobile devices and infer the logical consequences by providing timely responses and limiting the consumption of their resources. A case study has been arranged in order to evaluate the system's effectiveness within a mobile application for managing personal diets according to daily caloric needs.