Building virtual environment for feeding scenario simulation

作者: Dieter Wolke , Silvester Czanner , A. Petrasova , Alan Chalmers , J. V. Farrer

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摘要: Interactive virtual environments are becoming increasingly important in science, industry and the health field. In many cases watching a video or listening to sound speech is not enough for enhancing human’s ability of learning it necessary have good medium which interact with subject material. Understanding child’s response food key part effective feeding. Feeding problems community often related problematic caregiverinfant relationships can cause anxiety new parents. Therefore we direct our attention observe interaction between mother child provide an immersive experience introduce novel solution high-fidelity environment interactive therapy; accompanied by several attributes, aimed at stimulating human senses such as vision hearing, may affect quality during It also very define what kind realism required users this application.

参考文章(23)
Karol Myszkowski, Paul Debevec, Wolfgang Heidrich, Greg Ward, Summant Pattanaik, Erik Reinhard, High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting ,(2010)
Silvester Czanner, A. Petrasova, Alan Chalmers, Joung Hume Kwon, Mathematical modelling for development of egocentric virtual environments Slovak University of Technology in Bratislava. ,(2009)
Eitan M Glinert, The human controller : usability and accessibility in video game interfaces Massachusetts Institute of Technology. ,(2008)
Dieter Wolke, D. Skuse, S. Reilly, The nature and consequences of feeding problems in infancy Routledge. pp. 17- 50 ,(2006) , 10.4324/9780203770276-6
Hunter G. Hoffman, Todd L. Richards, Aric R. Bills, Trevor Van Oostrom, Jeff Magula, Eric J. Seibel, Sam R. Sharar, Using FMRI to study the neural correlates of virtual reality analgesia. Cns Spectrums. ,vol. 11, pp. 45- 51 ,(2006) , 10.1017/S1092852900024202
Hector Jasso, Jochen Triesch, A Virtual Reality Platform for Modeling Cognitive Development Biomimetic Neural Learning for Intelligent Robots. ,vol. 3575, pp. 211- 224 ,(2005) , 10.1007/11521082_12