Browsing by Author "Parente, G."

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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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    Formal Modeling Techniques for Ambient Assisted Living
    (Springer, 2010-11-23) Parente, G.; Nugent, Chris; Hong, X.; Donnelly, Mark P.; Chen, Liming; Vicario, E.
    In the development of systems of ambient assisted living (AAL), formalized models and analysis techniques can provide a ground that makes development amenable to a systematic approach. We consider the following formal modeling tools and techniques: fault trees, evidential reasoning, evidential ontology networks, temporal logic, hidden Markov models and partially observable Markov models. We review them in the perspective of their potential in the realm of AAL, recalling the general traits and potential of each of them, and highlighting how this can be concretely deployed within the AAL realm. To this end, we present a number of scenarios providing insight on how each technique can match the needs of different types of problem in the application domain.