Sherief Mowafey and Stephen Gardner
fuzzy logic systems, adaptive intelligent agents, ambient intelligent home care environments
The monitoring of individuals conditions in a purely medical sense, through the acquisition and interpretation of physiological data by a medical practitioner on a regular, personal basis, will not be a viable proposition in the future due to the projected numbers of individuals requiring such activity. The programme of work being developed in the University of Glamorgan defines a networked, wireless ambient data acquisition environment that serves as real world Ambient Intelligent Environment (AIE) test bed. Its objective is the classification and integration of the data into a knowledge based intelligent system to provide the mechanism of ubiquitous computing. This paper presents a framework in which those ideas can be applied and tested in a distributed architecture that will facilitate invasive observation and even a level of supervision through pervasive and intelligent supervisor interventions. Initial work that has been undertaken has attempted to infer the emotional state of an individual within a monitored ambient intelligent environment and these early findings are presented as an indicator of the potential of the architecture being developed. The knowledge abstraction mechanism and classification techniques focus on the use of fuzzy logic methodology where a non-intrusive intelligent learning and adaptation agent that could be embedded in Ambient Intelligent Environments will be discussed. This proposed agent learns and models the user behaviour in order to control the environment on their behalf with respect to his emotional state. In order to realise one of the main requirements of ambient intelligent systems, the agent was developed to act in an adaptive way where it will manage and control the environment on behalf of the user with respect to his emotional state as an attempt to understand the word “Well-being”. It will also allow the rules to be adapted and extended online, assisting a life-long learning technique as the environmental conditions changes and the user behaviour adjust with it.
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