Obesity is a real health emergency with significant consequences for individuals and society, reducing expectations and quality of life or even the death for 2.8 million people each year. Being a chronic condition with limited pharmacological options, weight loss therapies are mainly based on dietary and cognitive-behavioral interventions delivered at specialist public and/or private centers. In order to promote and facilitate weight loss, this paper proposes a virtual coaching system to motivate and guide the patients during the therapy and to support the clinicians in monitoring its effectiveness. The system has three components: a) the clinician's web application, which is used to monitor and modify the therapy for each patient; b) a mobile application for patients, used to log nutrition intakes and physical activities, and to consult motivational documents/videos; and c) a cloud server, which collects data and implements smart monitoring features. The platform is adopted in a clinical trial involving 120 patients with obesity (Body Mass Index 2> 30) that has recently started.

Different mining approaches have been proposed in literature for the automatic generation of temporal assertions from execution traces of digital systems. However, in most cases, existing tools can only mine assertions compliant with a limited set of pre-defined templates. Furthermore, they tend to generate a huge amount of assertions, while they still lack an effective way to measure their coverage in terms of design behaviours. To fill in the gap, this paper presents A-TEAM, a tool for the automatic extraction of temporal assertions starting from a set of user-defined assertion templates. Our method involves a combination of data mining and coverage analysis for mining a compact and expressive set of LTL formulas.