Object-oriented knowledge representation and discovery of human chewing behaviours

  • Authors:

  • W. L. Xu;L. Kuhnert;K. Foster;J. Bronlund;J. Potgieter;O. Diegel

  • Affiliations:

  • School of Engineering and Technology, Massey University, Private Bag 102 904, North Shore Mail Centre, Auckland, New Zealand;School of Engineering and Technology, Massey University, Private Bag 102 904, North Shore Mail Centre, Auckland, New Zealand;School of Engineering and Technology, Massey University, Private Bag 102 904, North Shore Mail Centre, Auckland, New Zealand;School of Engineering and Technology, Massey University, Private Bag 102 904, North Shore Mail Centre, Auckland, New Zealand;School of Engineering and Technology, Massey University, Private Bag 102 904, North Shore Mail Centre, Auckland, New Zealand;School of Engineering and Technology, Massey University, Private Bag 102 904, North Shore Mail Centre, Auckland, New Zealand

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2007

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Abstract

Mastication is a complex process influenced by numerous factors including those associated with an individual and the ingested food. Human chewing behaviour can be characterised by measuring mandibular movements and muscular activities during a masticatory sequence or by measuring the particle size distribution and rheological characteristics of the swallowed food mass. To constructively understand the mastication process and assess the mastication performance, a formal description of the chewing behaviour is proposed in this paper. An object-oriented model is developed and described in Unified Modelling Language (UML). The chewing behaviour model is composed of three objects, one for the jaw's physiological apparatus, one for the properties defining the mastication process and foods being chewed, and a further one for the association of the properties. A complete representation of the chewing behaviour is achieved by linking three object models via an additional class for chewing data that is collected experimentally. With the object model, the chewing behaviour is further instantiated by discovering knowledge hidden in the chewing database by data mining. A case study is presented to show the procedure of how the hidden knowledge is discovered and the data mining results are interpreted in the context of food science.