A model to predict hypovigilance during a monotonous task
Larue, Gregoire S., Rakotonirainy, Andry, & Pettitt, Anthony N. (2009) A model to predict hypovigilance during a monotonous task. In Proceedings of the 2009 Australasian Road Safety Research, Policing and Education Conference : Smarter, Safer Directions, Sydney Convention and Exhibition Centre, Sydney, New South Wales.
The driving task requires sustained attention during prolonged periods, and can be performed in highly predictable or repetitive environments. Such conditions could create drowsiness or hypovigilance and impair the ability to react to critical events. Identifying vigilance decrement in monotonous conditions has been a major subject of research, but no research to date has attempted to predict this vigilance decrement. This pilot study aims to show that vigilance decrements due to monotonous tasks can be predicted through mathematical modelling. A short vigilance task sensitive to short periods of lapses of vigilance called Sustained Attention to Response Task is used to assess participants’ performance. This task models the driver’s ability to cope with unpredicted events by performing the expected action. A Hidden Markov Model (HMM) is proposed to predict participants’ hypovigilance. Driver’s vigilance evolution is modelled as a hidden state and is correlated to an observable variable: the participant’s reactions time. This experiment shows that the monotony of the task can lead to an important vigilance decline in less than five minutes. This impairment can be predicted four minutes in advance with an 86% accuracy using HMMs. This experiment showed that mathematical models such as HMM can efficiently predict hypovigilance through surrogate measures. The presented model could result in the development of an in-vehicle device that detects driver hypovigilance in advance and warn the driver accordingly, thus offering the potential to enhance road safety and prevent road crashes.
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|Item Type:||Conference Paper|
|Keywords:||Monotony, Fatigue, Vigilance, Hidden Markov Models|
|Subjects:||Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Computer Perception Memory and Attention (170201)|
|Divisions:||Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute of Health and Biomedical Innovation
Past > Schools > Mathematical Sciences
Current > Schools > School of Psychology & Counselling
|Copyright Owner:||Copyright 2009 Please consult the authors.|
|Deposited On:||06 Dec 2009 22:33|
|Last Modified:||29 Feb 2012 14:03|
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