A Semantic View for Flexible Communication Models between Humans, Sensors and Actuators

In this work, we present last extensions done on H2ML, a new approach to model human interaction models inside intelligent environments at different abstraction levels. H2ML has been defined by considering the exigency to design Ambient Intelligence (Ami) applications, by assuring transparency, uniformity and abstractness in bridging multiple sensors properties to flexible and personalized actuators that interact with the human choosing appropriate media communication strategies. Within this aim, H2ML can be viewed as a methodological approach, based on markup languages and fuzzy theory, that provides semantic representation to human attitudes acquired by the intelligent environment. H2ML works in bi-directional way: if from an one hand it builds semantic models of human behavior, from the other hand enables to impact on the most suitable control strategy to perform at actuator side, conciliating the action with the most suitable media "facilitator".