Electromyogram signal based hypovigilance detection.

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%.

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