Activity Recognition in Pervasive Intelligent Environments

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Liming Chen, Chris D. Nugent, Jit Biswas, Jesse Hoey
Springer Science & Business Media, May 4, 2011 - Computers - 329 pages
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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.
 

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Contents

Approaches Practicesand Trends
1
2 A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimers Patients
33
3 Multiuser Activity Recognition in a Smart Home
59
a Logicbased Approach
83
An Interactive TVbasedAmbient Assisted Living Platform
111
6 An Ontologybased Contextaware Approach forBehaviour Analysis
127
7 Users Behavior Classification Model for Smart Houses Occupant Prediction
149
Benchmark and Software
165
9 Smart Sweet Home A Pervasive Environment for Sensing our Daily Activity?
187
10 Synthesising Generative Probabilistic Models forHighLevel Activity Recognition
209
11 Ontologybased Learning Framework forActivity Assistance in an Adaptive Smart Home
237
12 Benefits of Dynamically Reconfigurable ActivityRecognition in Distributed Sensing Environments
265
13 Embedded Activity Monitoring Methods
291
14 Activity Recognition and Healthier Food Preparation
313
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