Browsing by Author "Biswas, J."

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    Activity Recognition in Pervasive Intelligent Environments
    (Atlantis Press, 2011) Chen, Liming; Nugent, Chris; Biswas, J.; Hoey, J.
    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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    Comparison of Fusion Methods Based on DST and DBN in Human Activity Recognition
    (Springer, 2011-03-10) Tolstikov, A.; Hong, X.; Biswas, J.; Nugent, Chris; Chen, Liming; Parente, G.
    Ambient assistive living environments require sophisticated information fusion and reasoning techniques to accurately identify activities of a person under care. In this paper, we explain, compare and discuss the application of two powerful fusion methods, namely dynamic Bayesian networks (DBN) and Dempster-Shafer theory (DST), for human activity recognition. Both methods are described, the implementation of activity recognition based on these methods is explained, and model acquisition and composition are suggested. We also provide functional comparison of both methods as well as performance comparison based on the publicly available activity dataset. Our findings show that in performance and applicability, both DST and DBN are very similar; however, significant differences exist in the ways the models are obtained. DST being top-down and knowledge-based, differs significantly in qualitative terms, when compared with DBN, which is data-driven. These qualitative differences between DST and DBN should therefore dictate the selection of the appropriate model to use, given a particular activity recognition application.
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    An introduction to ontology-based activity recognition
    (ACM, 2009-12-14) Chen, Liming; Biswas, J.
    this is a tutorial.
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    Requirements for the Deployment of Sensor Based Recognition Systems for Ambient Assistive Living
    (Springer, 2010-06-22) Biswas, J.; Baumgarten, M.; Tolstikov, A.; Phyo Wai, A.A.; Nugent, Chris; Chen, Liming; Donnelly, Mark P.
    The deployment, replication and adaptation of sensor environments at various scales and for various purposes is one of the challenges academia as well as industry are faced with today. Individual user requirements, the heterogeneity of devices, the often non-standardized communication protocols in addition to proprietary-related aspects are just some of the problems, which must be addressed in order to establish flexible, efficient and maybe most importantly, cost-effective smart environments that are capable to facilitate Ambient Assisted Living (AAL). This paper discusses some of the requirements for the design and deployment of activity recognition systems and also addresses some of the problems that arose during the replication and adaptation of such a system along with a reflection on the lessons learnt.