Activity Recognition in Pervasive Intelligent Environments [electronic resource] /
Chen, Liming.editor.
Nugent, Chris D.editor.
Biswas, Jit.editor.
Hoey, Jesse.editor.
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.
Description based upon print version of record.
Activity Recognition in Pervasive Intelligent Environments; Preface; Contents; 1 Activity Recognition: Approaches, Practicesand Trends; 1.1 Introduction; 1.2 Activity recognition approaches and algorithms; 1.2.1 Activity recognition approaches; 1.2.2 Activity recognition algorithms; 1.2.3 Ontology-based activity recognition; 1.3 The practice and lifecycle of ontology-based activity recognition; 1.3.1 Domain knowledge acquisition; 1.3.2 Formal ontology modelling; 1.3.3 Semantic sensor metadata creation; 1.3.4 Semantic sensor metadata storage and retrieval; 1.3.5 Activity recognition
1.3.6 Activity model learning1.3.7 Activity assistance; 1.4 An exemplar case study; 1.5 Emerging research on activity recognition; 1.5.1 Complex activity recognition; 1.5.2 Domain knowledge exploitation; 1.5.3 Infrastructure mediated activity monitoring; 1.5.4 Abnormal activity recognition; 1.6 Conclusions; References; 2 A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer's Patients; 2.1 Introduction; 2.2 Overall Picture of Alzheimer's disease; 2.3 Related Work; 2.4 Possibilistic Activity Recognition Model
2.4.1 Environment Representation and Context2.4.2 Action Recognition; 2.4.3 Behavior Recognition; 2.4.4 Overview of the activity recognition process; 2.5 Smart Home Validation; 2.5.1 Results; 2.5.2 Discussion; 2.5.3 Summary of Our Contribution; 2.6 Conclusion; References; 3 Multi-user Activity Recognition in a Smart Home; 3.1 Introduction; 3.2 Related Work; 3.3 Multi-modal Wearable Sensor Platform; 3.4 Multi-chained Temporal Probabilistic Models; 3.4.1 Problem Statement; 3.4.2 Feature Extraction; 3.4.3 Coupled Hidden Markov Model; 3.4.4 Factorial Conditional Random Field
3.4.5 Activity Models in CHMM and FCRF3.5 Experimental Studies; 3.5.1 Trace Collection; 3.5.2 Evaluation Methodology; 3.5.3 Accuracy Performance; 3.6 Conclusions and Future Work; Acknowledgement; References; 4 Smart Environments and Activity Recognition:a Logic-based Approach; 4.1 Introduction; 4.2 Related Work; 4.3 Representation of Temporal Contexts; 4.4 Assessment of Temporal Contexts; 4.5 Experimental Results and Discussion; 4.5.1 An Example of System Usage; 4.5.2 A Discussion about context assessment complexity and system performance; 4.6 Conclusion; References
5 ElderCare: An Interactive TV-basedAmbient Assisted Living Platform5.1 Introduction; 5.2 Related Work; 5.3 The ElderCare Platform; 5.3.1 Eldercare Platform Components; 5.4 Implementation Overview; 5.4.1 Eldercare's Local System; 5.4.2 ElderCare's Central Server; 5.4.3 ElderCare's Mobile Client; 5.4.3.1 Data Encoding Features Analysis; 5.5 Conclusion and Further Work; Acknowledgements; References; 6 An Ontology-based Context-aware Approach forBehaviour Analysis; 6.1 Introduction; 6.2 Related Work; 6.3 Data Collection and Ontological Context Extraction; 6.3.1 Activity Context Extraction
6.3.2 Location Context Detection
Includes bibliographical references.
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.
Application software.
Optical data processing.
Information Systems Applications (incl. Internet).
Image Processing and Computer Vision.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Atlantis Ambient and Pervasive Intelligence