Section: New Results

Scenario Analysis Module

Participants : Annie Ressouche, Sabine Moisan, Jean-Paul Rigault, Daniel Gaffé, Omar Abdalla.

Keywords: Synchronous Modelling, Model checking, Mealy machine, Cognitive systems.

To generate activity recognition systems we supply a scenario analysis module (SAM) to express and recognize complex events from primitive events generated by SUP or other sensors. The purpose of this research axis is to offer a generic tool to express and recognize activities. Genericity means that the tool should accommodate any kind of activities and be easily specialized for a particular framework. In practice, we propose a concrete language to specify activities in the form of a set of scenarios with temporal constraints between scenarios. This language allows domain experts to describe their own scenario models. To recognize instances of these models, we consider the activity descriptions as synchronous reactive systems  [80] and we adapt usual techniques of synchronous modelling approach to express scenario behaviours. This approach facilitates scenario validation and allows us to generate a recognizer for each scenario model.

Setting up our tools on top of an existing language such as LUSTRE was convenient for rapid prototyping. However, it appeared delicate for efficiency reasons on the one hand, but also because it is a closed environment, difficult to customize. Hence we developed our own language le and its environment clem (see section  6.27 ). This year, we focus on the expression of scenario models in clem through the internship of Omar Adballa [51] and we define in clem a specific back end to generate recognition engines (see figure 37 ). However, mastering all aspects of this environment will allow the user scenario description language to rely directly on the semantics of le and not on its syntax. This reduces the number of necessary translations.

Currently, SAM implements an “exact” algorithm in the sense that it generates, at each instant, all possible scenario instances although many of them will freeze, still holding system resources. We have started scalability studies to evaluate the risk of combinatorial explosion. In parallel we enriched the synchronous scenario descriptions to reduce the number of generated scenario instances as well as the number of instances to awake at each instant. We are currently modifying our recognition engine generator to take advantage of this supplementaty information.