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. A formal description of the chewing behaviour is proposed in this chapter for use in constructively understanding the mastication process and assessing mastication performance. 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 the 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 can be discovered and the data mining results are interpreted in the context of food science.
Reprinted with modification from Xu WL, Kuhnert L, Foster K, Bronlund J, Potgieter J and Diegel O (2007) Object-oriented knowledge representation and discovery of human chewing behaviours. Eng. Appl. Artificial Intell. 20:1000-1012, with permission from Elsevier.
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Xu, W., Bronlund, J.E. (2010). Knowledge System of Human Chewing Behaviours. In: Mastication Robots. Studies in Computational Intelligence, vol 290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93903-0_10
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DOI: https://doi.org/10.1007/978-3-540-93903-0_10
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