Browsing by Author "Tolstikov, A."

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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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    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.