Abdul Syafiq Abdull Sukor
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Preferred name
Abdul Syafiq Abdull Sukor
Official Name
Abdull Sukor, Abdul Syafiq
Alternative Name
Abdull Sukor, Abdul Syafiq
Sukor, Abdul Syafiq Abdull
Sukor, Abdul Syafiq Bin Abdull
Abdull Sukor, A. S.
Sukor, A. S.A.
Main Affiliation
Scopus Author ID
57209073616
Researcher ID
L-8520-2019

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  • Publication
    A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes
    ( 2019)
    Abdul Syafiq Abdull Sukor
    ;
    Ammar Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Latifah Munirah Kamarudin
    ;
    Rossi Setchi
    ;
    Hiromitsu Nishizaki
    Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.
      19  16