Bayesian Network Model for Human Fatigue Recognition 


Vol. 30,  No. 9, pp. 887-898, Sep.  2005


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  Abstract

In this paper, we introduce a probabilistic model based on Bayesian networks (BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.

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  Cite this article

[IEEE Style]

Y. Lee, H. Park, C. Bae, "Bayesian Network Model for Human Fatigue Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 9, pp. 887-898, 2005. DOI: .

[ACM Style]

Young-sik Lee, Ho-sik Park, and Cheol-soo Bae. 2005. Bayesian Network Model for Human Fatigue Recognition. The Journal of Korean Institute of Communications and Information Sciences, 30, 9, (2005), 887-898. DOI: .

[KICS Style]

Young-sik Lee, Ho-sik Park, Cheol-soo Bae, "Bayesian Network Model for Human Fatigue Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 9, pp. 887-898, 9. 2005.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880