Biosignal Based Discrimination between Slight and Strong Driver Hypovigilance by Support-Vector Machines

In the area of transportation research, there is a growing need for robust and reliable measures of hypovigilance, particularly due to the current volume of research in the development and validation of Fatigue Monitoring Technologies (FMT). Most of the currently emerging FMT is vision based. The parameter Percentage of Eyelid Closure (PERCLOS) is used for the fatigue detection. The development and validation of PERCLOS based FMT require an independent reference standard of drivers’ hypovigilance. Most approaches utilized electrooculography (EOG) and electroencephalography (EEG) combined with descriptive statistics of a few time or spectral domain features. Typically, the power spectral densities (PSD) averaged in four to six spectral bands is used for fatigue characterization. This constricted approach led to sometimes contradicting results and questioned the validity of the EEG and EOG as gold standard for driver fatigue, wrongly as we will show. Here we present a more general approach using generalized EEG and EOG PSD features in combination with data fusion and advanced computational intelligence methods, such as Support-Vector Machines (SVM). Biosignal based discrimination of driver hypovigilance was performed by independent class labels which were derived from Karolinska Sleepiness Scale (KSS) and from variation of lane deviation (VLD). The first is a measure of subjectively self-experienced hypovigilance, whereas the second is an objective measure of performance decrements. For simplicity, two label classes were discriminated: slight and strong hypovigilance. The discrimination results of PERCLOS were compared with results from single and combined EEG and EOG channels. We conclude that EEG and EOG biosignals are substantially more suited to assess driver’s hypovigilance than the PERCLOS biosignals. In addition, computational intelligence performed better when objective class labels were used instead of subjective class labels.