The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지

Bayesian Network Model for Human Fatigue Recognition

피로 인식을 위한 베이지안 네트워크 모델

  • 이영식 (경동대학교 컴퓨터미디어공학부 전자상거래공학) ;
  • 박호식 (관동대학교 전자통신공학과 영상처리연구실) ;
  • 배철수 (관동대학교 전자통신공학과 영상처리연구실)
  • Published : 2005.09.01
Download PDF
⟨ Previous Next ⟩

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.

본 논문에서는 피로를 인식하기 위하여 베이지안 네트워크를 기반으로 한 확률 모델을 제안하고자 한다. 먼저 적외선 조명을 조사하여 눈거풀의 움직임, 시선 방향, 얼굴의 움직임 및 얼굴 표정 같은 얼굴특징정보를 측정하였다. 그러나 각각의 얼굴특징정보만으로 생체 피로를 결정하기에는 충분하지 않다. 그러므로, 본 논문에서는 생체 피로를 확률적 추론하기 위하여 가능한 많은 피로 원인에 대한 정보와 얼굴특징정보들로 베이지안 네트워크 모델을 구성하여 BN 피로지수를 산출하였다. 또한, BN 피로지수의 문턱치값은 MSBNX 시물레이션 결과 0.95로 산출되었다. 실험 결과 BN 피로지수와 TOVA 응답 시간을 비교한 결과 밀접한 상관관계가 있음을 확인하여 제안한 피로인식모델의 유효성을 입증하였다.

Keywords

References

  1. Julie H, Skipper, Walter W. Wierwille, 'An investigation of low-level stimulus-induced measures of driver drowsiness.', Proceedings of the Conference on Vision in Vehicles, pp.139-148, September, 1985
  2. David J. Mascord, Jeannie Walls and Graham A Starmer, 'Fatigue and Alcohol: interactive effects on human performance in driving- related tasks.', Fatigue and Driving. Taylor & Francis, pp.189-205, 1995
  3. 이상국, B. Decoux, R. Debrie, M. Hubin, 'Traffic security and detection of the driver's low vigilance sate.', 제6회 센서기술학술대회 논문집, 10/11, pp.54-62, Nov. 1995
  4. Boverie. S, Leqellec. J, and Hirl. A, 'Intelligent systems for video' monitoring of vehicle cockpit.', International Congress and Exposition ITS: Advanced Controls and Vehicle Navigation Systems, pp. 1-5, 1998
  5. Ueno. H, Kaneda. M, and Tsukino. M, 'Development of drowsiness detection system.', Proceedings of Vehicle Navigation and Information Systems conference, Yokohama, Japan, pp.15-20, August 1994 https://doi.org/10.1109/VNIS.1994.396873
  6. T. E. Hutchinson, 'Eye movement detection with improved calibration and speed.', United States Patent, (4,950,069), 1988
  7. Cortes. C, and Vapnik. V, 'Support-vector networks.', Machine Learning 20, pp.273-297, 1995
  8. Takchito. H, Katsuya. M, Kazunori. S and Yuji. M, 'Detecting drowsiness while driving by measuring eye movement - A polot study.', International Conference on Intelligent Transportation Sytstems, IEEE, 3-6 September 2002
  9. Qiang. J, and Xiaojie. Y, 'Real-time eye, gaze, and face pose tracking for monitoring driver vigilance.', Real-Time Imaging, Volume 8, Issue 5 pp.357-377, 2002 https://doi.org/10.1006/rtim.2002.0279
  10. Y. Tian, T. Kanade, and J. F. Cohn, 'Recognizing upper face action units for facial expression analysis.', In Proceedings of Conference on Computer Vision and Pattern Recognition, June 2000
  11. M. R. Rosekind, E. L. Co, K. B. Gregory, and D. L. Miller, 'Crew factors in flight operations xiii: A survey of fatigue factors in corporate/executive aviation operations,' National Aeronautics and Space Administration, Ames Research Center Moffett -Field, California 94035, NASA/TM-2000-209610, 2000
  12. E. L. Co, K. B. Gregory, J. M. Johnson, and M. R. Rosekind, 'Crew factors in flight operations xi: A survey of fatigue factors in regional airline operations.', National Aeronautics and Space Administration, Ames Research Center Moffett Field, California 94035, NASA/TM-1999-208799, 1999
  13. P. Sherry, 'Fatigue countermeasures in the railroad industry-past and current developments.', Counseling Psychology Program, Inter-modal Transportation Institute, University of Denver, 2000
  14. M. R. Rosekind, K. B. Gregory, E. L. Co, D. L. Miller, and D. F. Dinges, 'Crew factors in flight operations xii: A survey of sleep quantity and quality in on-board crew rest facilities.', National Aeronautics and Space Administration, Ames Research Center Moffett Field, California 94035, NASA/TM2000-209611, 2000
  15. F. V. Jensen, 'Bayesian networks and decision graphs,' Statistics for Engineering and Information Science, Springer, 2001
  16. Microsoft Research Center, 'online msbnx editor manual and software download,' http://research.microsoft.com/adapt/MSBNx/
  17. Zhiwei Zhu, Qiang Ji, 'Real time and non-intrusive driver fatigue monitoring,' IEEE Conference on ITS, pp.657-662, 2004
  18. Liangyu Lei, 'Fatigue life prediction of driving axle based on virtual prototype technology,' IEEE International Conference on Systems, Man and Cybernetics, pp. 3793-3798, 2004 https://doi.org/10.1109/ICSMC.2004.1400935
  19. 이영식, 배철수, '실시간 눈과 시선 위치 추적.', 한국해양정보 통신학회논문지, 8권 2호, 2004
  20. 박호식, 배철수, '휴먼 컴퓨터 인터페이스를 위한 실시간 시선 식별.', 한국통신학회논문지 30권, 3C호, 2005