| dc.description.abstract | In recent years, driver hypovigilance which includes driver drowsiness and driver
inattention is one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Reliable driver hypovigilance detection system which could alert the driver before a mishap happens would ensure less road accidents. Previous research works have reported only on detecting either drowsiness or inattention. 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. Researchers have attempted to determine driver drowsiness or driver inattention using the following
measures: (1) subjective measures, (2) vehicle-based measures, (3) behavioral measures
and (4) physiological measures. A detailed review on these measures as to the sensors
used, advantages and limitations associated with each measure is provided. The
different ways in which drowsiness and inattention has been experimentally
manipulated is also discussed. ECG and EMG signals are less intrusive as compared to
other physiological signals and provide true state of the driver. 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. A total of 15 male subjects participated in the data
collection process and drove for two hours in a simulated environment, at three
different times of the day (00:00 – 02:00 hours, 03:00 – 05:00 hours and 15:00 – 17:00
hours) when their circadian rhythm is low. 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 conventional statistical, higher
order statistical and higher order spectral features. Statistically significant differences
were observed between the alert, drowsy and inattentive states in both the physiological
signals. The features were classified using k nearest neighbor, linear discriminant
analysis and quadratic discriminant analysis. The energy feature of ECG signals gave a
maximum accuracy of 93.35 %. 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%. In case of drowsiness, the driver has to be alerted on time. Hence,
the different stages of drowsiness were classified with an overall accuracy of 71 %.
Alerting the driver during initial stage of drowsiness would minimize accidents. In the
future, the performance of hypovigilance detection system can be enhanced my
merging these physiological measures with behavioral measures and vehicle based
measures. A hybrid drowsiness detection system that combines non-intrusive
physiological measures with other measures would accurately determine the drowsiness
level of a driver. A number of road accidents can be avoided if an alert is sent to a
driver who is drowsy or inattentive. | en_US |
