Abstract:
Fatigue affects the drivers' ability to continue driving safely. Therefore, on-line monitoring of
physiological signals while driving provides the possibility of detecting fatigue in real time. The
EEG signal has been found to be the most predictive and reliable indicator. However, little
evidence exists on implementing EEG into a fatigue countermeasure device.
The aims were to utilise EEG changes during fatigue for development of fatigue
countermeasure software and to test the ability of such software in detecting fatigue. EEG was
obtained in twenty truck drivers during a driver simulator task till subjects fatigued. Changes
found in delta, theta, alpha and beta activity were used to develop algorithms for the software.
The software was designed to detect an alert state and early, medium and extreme levels of
fatigue. The software was tested in off-line mode in a group often truck drivers.
The software was capable of detecting fatigue accurately in all ten subjects. The percentage of
time the subjects were detected to be in a fatigue state was significantly different to the alert
phase (p<O.O1). For 40% of the total driving time subjects were alert and for 60% of the time,
the software detected one of the three fatigue states. In on-line analysis the software could alert
the three stages of fatigue.
The software could detect fatigue accurately. This is the first countermeasure software that can
detect fatigue based on EEG changes in all bands. Future field research is required with the
fatigue software to produce a robust and reliable fatigue countermeasure system.
