Information fusion with Bayesian networks for monitoring human fatigue

Abstract

In this paper, we introduce a probabilistic model based on Bayesian networks (BNs) for inferring human fatigue by integrating information from various visual cues and certain relevant contextual information. First, we briefly review the modern physiological and behavioral studies on human fatigue to identify the major causes for human fatigue and the significant factors affecting fatigue. These factors are then extracted from those studies and form the contextual information variables in our fatigue model. Visual parameters, typically characterizing the cognitive states of a person including parameters related to eyelid movement, gaze, head movement, and facial expression, serve as the sensory observations in the fatigue model. The fatigue model is subsequently parameterized based on the statistics extracted from recent studies on fatigue and on our 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 inference results produced by running the fatigue model using Microsoft BNs engine MSBNX demonstrate the utility of the proposed framework for predicting and modeling fatigue.