Summary
Technostress is the general term of the malfunction condition which treating the information management system accompanied by VDTs (Visual Display Terminals). The applied technology of ubiquitous computing will enhance the affinity between a computer and person. However, continuous use of VDTs sometimes causes technostress to us. The purpose of this study was to establish the method of reducing the visual fatigue during VDT work. In order to obtain a better reasoning result, it is necessary to deal with environmental information, VDT information and user information such as both psychological and physiological information, comprehensively. We, then, represent the causal association of visual fatigue using Bayesian network. Result showed the causal relationship between visual fatigue and VDT work factors visually.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tennenhouse, D (May. 2000). Proactive computing, Communications of ACM, 43:43–50
Abowd, G.D. and Mynatt, E.D (2000) Charting Past, Present, and Future Research in Ubiquitous Computing, ACM Transactions on Computer-Human Interaction, 7:29–58
Minister of Labor secretariat Policy Planning and Research Department (1999) Survey result news flash about technical innovation and labor_The Ministry of Labor’s announcement data
Fukuta, K., Koyama, T. and Uozumi, T (2004) [Estimate of degree of fatigue and improvement of VDT environment using Bayesian Network][Article in Japanese], Proceeding of Bayesian network seminar 2004
Fujisawa, K., Kakimoto, S. and Kawasaki, K (1998) [New physiology and psychology, Volume 1][Article in Japanese], pp.266–279, KITAOUJI Shobou
Intel Open source Computer Vision Library_ http://www.intel.com/research/mrl/research/opencv/
Matsubara, T (2002) [A guide to methods of a psychological test][Article in Japanese], Nihon Bunka Kagakusha, Tokyo, pp229–232
Russell, S. and Norvig, P (2002) Artificial Intelligence: A Modern Approach, 2nd Edition, pp.113–114, Pearson Education, Inc.
H. Akaike (1973) Information theory and an extension of the maximum likelihood principle, 2nd Int. Symposium on Information Theory, eds. Pertrov, B. N. and Csaki, F., pp.267–281, Akademia Kiado, Budapest
Nakaishi, H (Nov. 1995) [Lecture of work associated diseases: Eye disease][Article in Japanese], Industrial medical journal 18:12–17
Pearl, J (1986) Fusion, Propagation, and Structuring in Belief Networks, Artificial Intelligence, 29: 241–288
Heckerman, D (Mar. 1995) A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fukuta, K., Koyama, T., Uozumi, T. (2005). Representation of visual fatigue during VDT work using Bayesian network. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_64
Download citation
DOI: https://doi.org/10.1007/3-540-32391-0_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
Online ISBN: 978-3-540-32391-4
eBook Packages: EngineeringEngineering (R0)