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.