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titleA Probabilistic Framework for Modeling and Real-Time Monitoring Human Fatigue
authorsQiang Ji, Peilin Lan, and Carl Looney
keywordsfacial expression recognition, Bayesian Networks, human fatigue monitoring, information fusion, Dynamic Bayesian Networks, and Vigilance Test
abstractWe introduce a probabilistic framework based
on the Bayesian Networks (BNs) for modeling and real-time
inferring human fatigue by integrating information from various sensory data and certain relevant contextual information. We first present 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. 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 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 and accurate
fatigue modeling and inference.
A real time non-intrusive fatigue monitor was built based
on integrating the proposed fatigue model with a computer
vision system we 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.
typeJournal Paper
journalIEEE Transcations on Systems, Man, and Cybernetics A
published year
(Total records:1429)
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