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Comparing Contribution of Algorithm Based Physiological Indicators for Characterisation of Driver Drowsiness

Manuel Rost1,2, Eugene Zilberg1, Zheng Ming Xu1 , Yue Feng1,2, David Burton1, and Sara Lal2
1.Compumedics Medical Innovation Pty Ltd, 30-40 Flockhart St Abbotsford 3067 Australia
2.Neuroscience Research Unit, School of Medical and Molecular Biosciences, University of Technology Sydney (UTS), Broadway NSW 2007 Australia
Abstract—The algorithm based physiological characteristics of driver drowsiness – ocular parameters (derived from the frontal electroencephalogram (EEG)), EEG alpha bursts and spectral power (derived from the central and occipital sites) as well as heart rate variability (HRV) were estimated from data derived during a driving simulator experiment (30 non-professional drivers). The statistical associations of these parameters with the “gold standards” of driver drowsiness were investigated using linear regression and linear mixed models. The statistical models were also examined for a number of hybrid algorithms, which combined multiple characteristics of driver drowsiness. A combination of ocular parameters showed the strongest association (R2=0.48) with the applied trained observer rating (TOR) method; followed by EEG alpha bursts indicators (R2=0.30) and EEG spectrum data (R2=0.21). The HRV parameters showed a weak association (R2=0.04) A joint model including the eye parameters and the EEG alpha bursts resulted in the highest R2=0.54 to TOR. The results indicate that a hybrid automatic algorithm, based on multiple characteristics of the eye blinks and EEG patterns, but not necessarily including the HRV measures, is likely to achieve a level of accuracy in characterising driver drowsiness similar to that of a trained observer.

Index Terms—driver, drowsiness, fatigue, physiological, EEG, alpha bursts, eye behaviour, automatic, hybrid

Cite: Manuel Rost, Eugene Zilberg, Zheng Ming Xu, Yue Feng, David Burton, and Sara Lal, "Comparing Contribution of Algorithm Based Physiological Indicators for Characterisation of Driver Drowsiness," Journal of Medical and Bioengineering, Vol. 4, No. 5, pp. 391-398, October 2015. Doi: 10.12720/jomb.4.5.391-398
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