Service Adaptation Using Fuzzy Theory in Context-Aware Mobile Computing Middleware
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Towards Recognizing Emotion with Affective Dimensions Through Body Gestures
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Affect and Emotion in Human-Computer Interaction
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Developing context-aware pervasive computing applications: Models and approach
Pervasive and Mobile Computing
Argumentation-based decision making in ambient intelligence environments
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Distributing emotional services in Ambient Intelligence through cognitive agents
Service Oriented Computing and Applications
A multi-agent approach to controlling a smart environment
Designing Smart Homes
Improving game bot behaviours through timed emotional intelligence
Knowledge-Based Systems
Using ANFIS and FML for Deploying Transparent Services in Smart Environments
IMIS '12 Proceedings of the 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
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Ambient Intelligence (AmI) carries out a futuristic vision of living environments which are sensitive and responsive to the presence of people and, by taking care of their desires, intelligently respond to their actions improving their comfort and well-being. Typically, AmI frameworks are based on distributed context-aware approaches that, by using collections of invisible and interconnected devices, elicit and analyze environmental knowledge for delivering an appropriate set of services. Emotion-aware AmI (AmE) enhances the conventional idea of intelligent environment by exploiting theories from psychology and social sciences for suitably analyzing human emotional status and achieving a higher users' satisfaction. This work proposes a novel approach of combining emotion-aware idea with a neuro-fuzzy framework to train a collection of intelligent FML-based agents aimed at delivering efficient, personalized and interoperable emotion services in an AmE environment. As will be shown in experimental results, where a usability study and a confirmation of expectations test have been performed, the proposed approach is capable of anticipating user's requirements and improving the performance of a conventional AmI framework.