Activity Recognition in Pervasive Intelligent Environments
Samenvatting
This book consists of a number of chapters addressing different aspects of activity recognition, roughly in three main categories of topics. The first topic will be focused on activity modeling, representation and reasoning using mathematical models, knowledge representation formalisms and AI techniques. The second topic will concentrate on activity recognition methods and algorithms. Apart from traditional methods based on data mining and machine learning, we are particularly interested in novel approaches, such as the ontology-based approach, that facilitate data integration, sharing and automatic/automated processing. In the third topic we intend to cover novel architectures and frameworks for activity recognition, which are scalable and applicable to large scale distributed dynamic environments. In addition, this topic will also include the underpinning technological infrastructure, i.e. tools and APIs, that supports function/capability sharing and reuse, and rapid development and deployment of technological solutions. The fourth category of topic will be dedicated to representative applications of activity recognition in intelligent environments, which address the life cycle of activity recognition and their use for novel functions of the end-user systems with comprehensive implementation, prototyping and evaluation. This will include a wide range of application scenarios, such as smart homes, intelligent conference venues and cars.
Specificaties
Inhoudsopgave
Possibilistic Activity Recognition.
Multi-user Activity Recognition in a Smart Home.
Smart Environments and Activity Recognition: a Logic-based Approach.
ElderCare: An Interactive TV-based Ambient Assisted Living Platform.
An Ontology-based Context-aware Approach for Behaviour Analysis.
User’s Behavior Classification Model for Smart Houses Occupant Prediction.
Activity Recognition Benchmark.
Smart Sweet Home.
Synthesising Generative ProbabilisticModels for High-Level Activity Recognition.
Ontology-based Learning Framework for Activity Assistance in an Adaptive Smart Home.
Benefits of Dynamically Reconfigurable Activity Recognition in Distributed Sensing Environments.
Embedded Activity Monitoring Methods.
Activity Recognition and Healthier Food Preparation.
Activity Recognition: Approaches, Practices and Trends.
Possibilistic Activity Recognition.
Multi-user Activity Recognition in a Smart Home.
Smart Environments and Activity Recognition: a Logic-based Approach.
ElderCare: An Interactive TV-based Ambient Assisted Living Platform.
An Ontology-based Context-aware Approach for Behaviour Analysis.
User’s Behavior Classification Model for Smart Houses Occupant Prediction.
Activity Recognition Benchmark.
Smart Sweet Home.
Synthesising Generative ProbabilisticModels for High-Level Activity Recognition.
Ontology-based Learning Framework for Activity Assistance in an Adaptive Smart Home.
Benefits of Dynamically Reconfigurable Activity Recognition in Distributed Sensing Environments.
Embedded Activity Monitoring Methods.
Activity Recognition and Healthier Food Preparation.
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