Statistical validation of physiological indicators for non-invasive and hybrid driver drowsiness detection system

A hybrid system for detecting driver drowsiness was examined by using piezofilm movement sensors integrated into the car seat, seat belt and steering wheel. Statistical associations between increase in the driver drowsiness and the non-invasive and conventional physiological indicators were investigated. Statistically significant associations were established for the analysed physiological indicators – car seat movement magnitude and (electroencephalogram) EEG alpha band power percentage. All of the associastions were physiologically plausible with increase in probability of drowsiness associated with increases in the EEG alpha band power percentage and reduction in the seat movement magnitude. Adding a non-invasive measure such as seat movement magnitude to any combination of the EEG derived physiological predictors always resulted in improvement of associations. These findings can serve as a foundation for designing the vehicle-based fatigue countermeasure device as well as highlight potential difficulties and limitations of detection algorithm for such devices.