Neural Network Based Drowsiness Detection System Using Physiological Parameters

Abstract— In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures. (2) behavioural measures and (3) physiological measures. EEG signals acts as an indicator for the state of brain, and when the driver gets drowsy, α and θ waves become predominant. The main issue in such a technique is to extract a set of features that can highly differentiate between the different drowsiness levels. In this work, a new system for drivers drowsiness detection based on EEG using Neural network is proposed. This uses physiological data of drivers to detect drowsiness. These include the measurement of EEG and feature extraction in time and frequency domain. The proposed method was tested and validated on known set of standard data. Keywords— driver, drowsiness, eeg, spectral, neural network