Section: New Results

Scenario Analysis Module

Participants : Annie Ressouche, Daniel Gaffé, Narjes Ghrairi, Sabine Moisan, Jean-Paul Rigault.

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.

Since last year, we relied on clem (see section  6.25 ) synchronous language to express the automata semantics of scenario models as Boolean equation systems. This year, we continue our research in this direction and we are studying a specific semantics of SAM language operators that translates any SAM program into Boolean equation system. Therefore, we will benefit from clem compilation technique to generate recognizer for each scenario model.

This year we focus on the definition of an execution machine able to transform asynchronous events coming from SUP or other devices into synchronous significant events feeding recognition engines generated by SAM. The execution machine can listen three types of asynchronous events: SUP events, Boolean sensors, sampled sensors and pulse train sensors. According to the sampling period of each sensor, the execution machine builds the significant events defining the synchronous logical instants which trigger the reaction of the scenario recognition engine. Thanks to the synchronous approach, scenario recognition engines are able to dynamically express the expected synchronous events of the next step; the execution machine takes into account of this information to filter relevant events. We perform several tests with real SUP data sets and the execution machine has a convincing behaviour (see [55] ). To complement this work, we will integrate a notion of incompatible events which will make the execution machine more efficient and robust.