Assessing Driver’s Hypovigilance from Biosignals
For the assessment of Fatigue Monitoring Technologies (FMT) an independent reference of driver’s hypovigilance is needed. To achieve this goal, we propose to process EEG and EOG biosignals, to apply a feature fusion concept and to utilize Support-Vector Machines (SVM) for classification. Karolinska Sleepiness Scale (KSS) and variation of lane deviation (VLD) were used in order to get independent class labels, whereas KSS are subjective and VLD are objective measures. For simplicity, two classes were determined: slight and strong hypovigilance. 16 young volunteers participated in overnight experiments in our real car driving simulation lab. Results were compared with PERCLOS (percentage of eye closure), an oculomotoric variable utilized in several FMT systems. We conclude that EEG and EOG biosignals contain substantial higher amount of hypovigilance information than the PERCLOS biosignal.
KeywordsHypovigilance EEG EOG PERCLOS Data Fusion Support-Vector Machines Driving Simulation
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