Applying neural network analysis on heart rate variability data to assess driver fatigue

Authors:
M. Patel;S. K. L. Lal;D. Kavanagh;P. Rossiter
Affiliations:
University of Technology Sydney, 15 Broadway, Ultimo 2007, Australia;University of Technology Sydney, 15 Broadway, Ultimo 2007, Australia;University of Technology Sydney, 15 Broadway, Ultimo 2007, Australia;Forge Group, 1st Floor, 241 Broadway, Ultimo 2007, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver's alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.