Incorporating Human Aspects in Ambient Intelligence and Smart Environments

Incorporating Human Aspects in Ambient Intelligence and Smart Environments

Tibor Bosse (Vrije Universiteit Amsterdam, The Netherlands), Mark Hoogendoorn (Vrije Universiteit Amsterdam, The Netherlands), Michel Klein (Vrije Universiteit Amsterdam, The Netherlands), Rianne van Lambalgen (Vrije Universiteit Amsterdam, The Netherlands), Peter-Paul van Maanen (Vrije Universiteit Amsterdam, The Netherlands) and Jan Treur (Vrije Universiteit Amsterdam, The Netherlands)
DOI: 10.4018/978-1-61692-857-5.ch008
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Abstract

In this chapter, we propose to outline the scientific area that addresses Ambient Intelligence applications in which not only sensor data, but also knowledge from the human-directed sciences such as biomedical science, neuroscience, and psychological and social sciences is incorporated. This knowledge enables the environment to perform more in-depth, human-like analyses of the functioning of the observed humans, and to come up with better informed actions. A structured approach to embed human knowledge in Ambient Intelligence applications is presented an illustrated using two examples, one on automated visual attention manipulation, and another on the assessment of the behaviour of a car driver.
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Framework For Reflective Coupled Human-Environment Systems

One of the challenges is to provide frameworks that cover the class of Ambient Intelligence applications showing human-like understanding and supporting behaviour; see also (Treur, 2008). Here human-like understanding is defined as understanding in the sense of being able to analyse and estimate what is going on in the human’s mind (a form of mindreading) and in his or her body (a form of bodyreading). Input for these processes are observed information about the human’s state over time, and dynamic models for the human’s physical and mental processes. For the mental side such a dynamic model is sometimes called a Theory of Mind (e.g., Baron-Cohen, 1995; Dennett, 1987; Gärdenfors, 2003; Goldman, 2006) and may cover, for example, emotion, attention, intention, and belief. Similarly for the human’s physical processes, such a model relates, for example, to skin conditions, heart rates, and levels of blood sugar, insulin, adrenalin, testosterone, serotonin, and specific medicines taken. Note that different types of models are needed: physiological, neurological, cognitive, emotional, social, as well as models of the physical and artificial environment.

Key Terms in this Chapter

Visual Attention Manipulation: the task of tracking and steering the visual attention of a human related to the requirements of the task

Domain Model: a model of the interaction between the environment and the human in some scenario subject to support via ambient intelligence

Technological Awareness: awareness by the environment about the human and environmental processes and their interaction

Tactical Picture Compilation: the task in a military context to identify and classify all entities in the environment by reasoning with the available information, e.g. to determine the threat of a entity.

Situation Awareness: the perception of environmental elements within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future

Support Model: a model that allows for generating support for the human by reasoning based on the domain model

Driver Behaviour: N/A (general term)

Human Awareness: awareness by the human about the human and environmental processes and their interaction

LEADSTO: a specification language that can be used to formally describe simulation models in a declarative manner, combining numerical and logical statements.

Analysis Model: a model that allows for analysis of the human’s states and processes by reasoning based on observations (possibly using specific sensors) and a domain model

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