A probabilistic framework based on the Bayesian networks for modeling and real-time inferring human fatigue by integrating information from various sensory data and certain relevant contextual information is introduced. A static fatigue model that captures the static relationships between fatigue, significant factors that cause fatigue, and various sensory observations that typically result from fatigue is first presented. Such a model provides mathematically coherent and sound basis for systematically aggregating uncertain 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 fatigue model is extended based on dynamic Bayesian networks. The dynamic fatigue model allows to integrate fatigue evidences not only spatially but also temporally, therefore, leading to a more robust and accurate fatigue modeling and inference. A real-time nonintrusive fatigue monitor was built based on integrating the proposed fatigue model with a computer vision system developed for extracting various visual cues typically related to fatigue. Performance evaluation of the fatigue monitor using both synthetic and real data demonstrates the validity of the proposed fatigue model in both modeling and real-time inference of fatigue. (Author/publisher)
Abstract