Non-Intrusive Real Time Human Fatigue Modelling and Monitoring

Abstract

In this paper, we introduce a probabilistic model based on the Dynamic Bayesian Networks (DBNs) for dynamically modelling and detecting human fatigue. We first present a static fatigue model that captures the static relationships between fatigue, significant factors that cause fatigue, and various visual cues that are typically resulted from fatigue. The static fatigue model allows to spatially integrate fatigue evidences from different sources. It, however, fails to capture the dynamic aspect of fatigue. Fatigue is a cognitive state that is developed over time. To account for the temporal aspect of human fatigue, the static fatigue model is extended based on the DBNs. The dynamic fatigue model allows to integrate fatigue evidences not only spatially but also temporally, therefore leading to a more robust fatigue modelling and inference. The evaluation of the fatigue model with simulated data reveals the satisfactory performance of the proposed fatigue model. The dynamic fatigue model is then integrated with our computer vision module to perform non-intrusive real-time fatigue monitoring and detection.

Get full access to this article

View all access options for this article.

References

National Transportation Safety Board. Evaluation of us department of transportation efforts in the 1990s to address operator fatigue. Safety Report NTSB/SR-99/01, National Transportation Safety Board, May 1999. PB99-91 7002 Notation 7155.

E. L.

Gregory

K. B.

Johnson

J. M.

Rosekind

M. R.

Crew factors in flight operations xi: A survey of fatigue factors in regional airline operations. Technical Report NASA/TM-1999–208799, National Aeronautics and Space Administration, Ames Research Center Moffett Field, California 94035, October 1999.

Czeisler

C. A.

. Lecture on fatigue. In Fatigue Symposium Proceedings, page 59. National Transportation Safety Board and NASA Ames Research Center, November 1995.

Hartley

Horberry

Mabbott

Krueger

G. P.

Review of fatigue detection and prediction technologies. Technical Report ISBN 0 642 54469 7, National Road Transport Commission, Level 5/326 William Street MELBOURNE VIC 3000, September 2000. www.nrtc.gov.au.

Jensen

F. V.

. Bayesian Networks and Decision Graphs, Statistics for Engineering and Information Science. Springer, 2001.

Qiang

Yang

Xiaojie

. Real time visual cues extraction for monitoring driver vigilance. In International Workshop on Computer Vision Systems, Vancouver, Canada, July 2001.

Qiang

Yang

Xiaojie

. A computer vision system for real time driver alertness monitoring. Real Time Imaging, 2002. in Press.

Lan

Peilin

Qiang

Looney

Carl

. Information fusion with bayesian belief networks for monitoring human fatigue. In Proceedings of the 5th International Conference on Information Fusion (FUSION 2002), Annapolis, Maryland, July 2002. International Society of Information Fusion.

Mihaljlovic

Petkovi

Dynamic bayesian networks: A state of the art, computer science department. Technical report, Computer Science Department, University of Twente, The Netherlands, 2001.

10.

Rosekind

M. R.

. Physiological considerations of fatigue. In Fatigue Symposium Proceedings, page 17. National Transportation Safety Board and NASA Ames Research Center, November 1995.

11.

Rosekind

M. R.

E. L.

Gregory

K. B.

Miller

D. L.

Crew factors in flight operations xiii: A survey of fatigue factors in corporate/executive aviation operations. Technical Report NASA/TM-2000–209610, Ames Research Center Moffett Field at National Aeronautics and Space Administration, California 94035, September 2000.

12.

Rosekind

M. R.

Gregory

K. B.

E. L.

Miller

D. L.

Dinges

D. F.

Crew factors in flight operations xii: A survey of sleep quantity and quality in on-board crew rest facilities. Technical Report NASA/TM-2000–209611, National Aeronautics and Space Administration, Ames Research Center Moffett Field, California 94035, September 2000.

13.

Schafer

Weyrath

Assessing temporally variable user properties with dynamic baysian networks. In User Modelling: Proceeding of the Sixth International Conference, pages 377–388, 1997.

14.

Sherry

. Fatigue countermeasures in the railroad industry-past and current developments. Technical report, Counselling Psychology Program, Inter-modal Transportation Institute, University of Denver, June 2000.

15.

Wylie

C. D.

Shultz

Miller

J. C.

Mitler

M. M.

Mackie

R. R.

Commercial motor vehicle driver fatigue and alertness study: Technical summary. Technical Report FHWA-MC-97–001, Trucking Research Institute, American Trucking Associations Foundation, Transportation Development Center, Safety and Security Transport Canada, November 1996. TC report number: TP 12876E.