Ye, Juan

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Ye, Juan
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Ye, Juan

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  • Publication
    Activity recognition using temporal evidence theory
    (IOS Press, 2010)
    McKeever, Susan 
    ;
    Ye, Juan 
    ;
    Coyle, Lorcan 
    ;
    Bleakley, Chris J. 
    ;
    Dobson, Simon 
    The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. People's activities in the home often have an identifiable routine; activities place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.
    Scopus© Citations 76  1244
  • Publication
    PI : perceiver and interpreter of smart home datasets
    (IEEE, 2011-05-23)
    Ye, Juan 
    ;
    Stevenson, Graeme 
    ;
    Dobson, Simon 
    ;
    O'Grady, Michael J. 
    ;
    O'Hare, G. M. P. (Greg M. P.) 
    Pervasive healthcare systems facilitate various aspects of research including sensor technology, software technology, artificial intelligence and human-computer interaction. Researchers can often benefit from access to real-world data sets against which to evaluate new approaches and algorithms. Whilst more than a dozen data sets are currently publicly available, their use of heterogeneous mark-up impedes easy and widespread use. We describe PI – the Perceiver and semantic Interpreter – which offers a workbench API for the querying, re-structuring and re-purposing of a range of diverse data formats currently in use. The use of a single API reduces cognitive overload, improves access, and supports integration of generic and domain-specific information within a common framework.
      748