A Probabilistic Framework for Modelling and Real-Time Monitoring Human Fatigue

In this paper, we introduce a probabilistic framework based on the Bayesian Networks (BNs) for modelling and real-time inferring human fatigue by integrating information from various visual cues and certain relevant contextual information. We first present a static Bayesian network that captures the static relationships between fatigue, significant factors that cause fatigue, and various visual cues that typically result from fatigue. The static fatigue model is subsequently parameterized based on the statistics extracted from recent studies on fatigue and on subjective knowledge. Such a model provides mathematically coherent and sound basis for systematically aggregating visual evidences from different sources, augmented with relevant contextual information. The static model, 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 BNs model is extended based on the Dynamic Bayesian Networks (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. This paper also includes a review of modern physiological and behavioral studies on human fatigue and a detailed critical review of the existing techniques for Peilin Lan, Department of Computer Science, University of Nevada at Reno, NV 89557, USA. Qiang Ji, Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. Carl G. Looney, Department of Computer Science, University of Nevada at Reno, NV 89557, USA. Questions about this paper, please address to Dr. Qiang Ji at [email protected] fatigue monitoring and detection. The paper ends with a discussion of the interface program we developed, that combines our computer vision system for visual cues extraction with the fatigue inference engine for real-time non-intrusive human fatigue monitoring and detection.