Drowsy Driving Detection System by Analyzing and Classifying Brain Waves

Abstract

The increase in number of accidents is a dangerous situation that needs to be mitigated. If the data from recent past is taken into account, one can see that the number of road accidents has grown exponentially. In fact, roadways record the highest number of accidents in comparison to other modes of transport. Although drowsy driving is not the only contributor, it remains the principal or major issue and can pose a grave threat if it is not averted. This paper thus aims to introduce a method to reduce the number of accidents due to drowsy driving. Using an Electroencephalograph (EEG) headset which consists of multiple EEG sensors and inbuilt Bluetooth transmitter, brain wave data is transmitted to the system. The collected data is fed as input to the prediction model which decides whether the driver is being drowsy or not. The prediction model is trained by using the Auto Regressive and Integrated Moving Average (ARIMA) time series algorithm to predict whether the person is being drowsy. If so, the proposed system can be used to trigger an alarm/warning mechanism.