Abstract:
This paper describes a driver fatigue detection
system using an Artificial Neural Network (ANN). Using
electroencephalogram (EEG) data sampled from 20
professional truck drivers and 35 non professional drivers, the
time domain data are processed into alpha, beta, delta and
theta bands and then presented to the neural network to detect
the onset of driver fatigue. The neural network uses a training
optimization technique called the Magnified Gradient Function
(MGF). This technique reduces the time required for training
by modifying the Standard Back Propagation (SBP) algorithm.
The MGF is shown to classify professional driver fatigue with
81.49% accuracy (80.53% sensitivity, 82.44% specificity) and
non-professional driver fatigue with 83.06% accuracy (84.04%
sensitivity and 82.08% specificity).
