Physiological signal based detection of driver hypovigilance using higher order spectra

In this work, the focus is on developing a system that can detect hypovigilance, which includes both drowsiness and inattention, using Electrocardiogram (ECG) and Electromyogram (EMG) signals. Drowsiness has been manipulated by allowing the driver to drive monotonously at a limited speed for long hours and inattention was manipulated by asking the driver to respond to phone calls and short messaging services. ECG and EMG signals along with the video recording have been collected throughout the experiment. The gathered physiological signals were preprocessed to remove noise and artifacts. The hypovigilance features were extracted from the preprocessed signals using higher order spectral features. The features were classified using k Nearest Neighbor, Linear Discriminant Analysis and Quadratic Discriminant Analysis. The bispectral features gave an overall maximum accuracy of 96.75% and 92.31% for ECG and EMG signals, respectively using k fold validation. The features of ECG and EMG signals were fused using principal component analysis to obtain the optimally combined features and the classification accuracy was 96%. A number of road accidents can be avoided if an alert is sent to a driver who is drowsy or inattentive.