Levels of Abstraction for Behavior Modeling in the GerHome Project

Levels of Abstraction for Behavior Modeling in the GerHome Project

Laura Pomponio (Centre Scientifique et Technique du Bâtiment & Aix-Marseille University, France), Marc Le Goc (Aix-Marseille University, France), Alain Anfosso (Centre Scientifique et Technique du Bâtiment, France) and Eric Pascual (Centre Scientifique et Technique du Bâtiment, France)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/jehmc.2012070102
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Abstract

Defining activity models in order to monitor human behavior in smart environments is one of the major issues at the moment of building systems of activity supervision for diagnosis, prediction and control. For the purpose of addressing this problem, this paper proposes a general theoretical approach based on the use of a Knowledge Engineering methodology and a Machine Learning process, which are funded on a general theory of dynamic process modeling, the Timed Observation Theory.
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Human activity recognition in perceptual environments involves severe challenges due to the erratic nature of human behavior. To determine what is being done can be complicated if different activities are executed at the same time; e.g., to cook while watching TV. Besides, the same detected action can be associated with several activities depending on the context in which it is carried out then, to discriminate what is the right activity is not trivial; e.g., to open sink water tap can be part of cooking or washing dishes. Moreover, activities can be interleaved: while washing dishes the phone rings, the activity is paused, the phone is answered and then, the activity is taken up again. Thus, to determine what a person is doing at a particular time is not a simple task.

The problem lies in the meaning and the interpretation of the perceptual inputs due to the large gap that exists between the low level signals, as pixels, sensor signals, etc., and that one that is inferred in a higher level; for example, washing dishes.

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