In recent years, driver drowsiness and driver inattention are the major causes for road accidents leading to severe traumas such as physical injuries, deaths, and economic losses. This necessitates the need for a system that can alert the driver on time, whenever he is drowsy or inattentive. Previous research works report the detection of either drowsiness or inattention only. In this work, we aim to develop a system that can detect hypovigilance, which includes both drowsiness and inattention, using Electromyogram (EMG) signals. Fifteen male volunteers participated in the data collection experiment where they were asked to drive for two hours at 3 different times of the day (00:00 – 02:00 hrs, 03:00 – 05:00 hrs and 15:00 – 17:00 hrs) when their circadian rhythm is low. The results indicate that the standard deviation feature of EMG is efficient to detect hypovigilance with a maximum classification accuracy of 89%.

The term ‘Hypovigilance’ is derived from two words ‘Hypo’ & ‘Vigilance’. ‘Hypo’ originates from a Greek word meaning ‘diminished’ and ‘vigilance’ means ‘alertness’. So, ‘hypovigilance’ together means ‘diminished alertness,’ and can be defined as anything that causes a decrease in paying a close and continuous attention. Impairment of alertness in a driver may be due to prolonged sleepiness or short term inattention. It may lead the driver to lose control of the vehicle which in turn can lead to accidents like crashing of the vehicle onto other vehicles or stationary surroundings. In order to prevent these devastating incidents, the state of the driver should be continuously monitored.

Monitoring driver behavior is a much needed factor for safe driving as driver drowsiness and driver inattention are the major causes for road accidents. Though researchers have probed into either drowsiness or inattention, not one of them has worked on a universal system to detect both drowsiness and inattention. In this work, hypovigilance has been detected using EMG signals. The raw signals being prone to noise were preprocessed using efficient filtering methods. The standard deviation feature of the preprocessed EMG signal has been classified into the various hypovigilance states such as fatigue, normal and inattention successfully with accuracy of 89.52%. Based on the outcome of this research study, a system to detect the hypovigilance and alert the driver can be worked out. The EMG measure can also be combined with other nonintrusive physiological measures like ECG for reliable results. In the future, behavioral measures and vehicle based measures can also be fused with physiological measures for better and robust detection.

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