Driver's Drowsiness Estimation with EEG Analysis Using Lab View
Author
S.Sundar, G.Suresh Kumar, S.Saravana Kumar
Bannari Amman Institute of Technology, Sathyamangalam
Abstract
Increasing number of traffic accidents has been a great threat to the public security. Majority of the reports indicates that it is due to the driver’s drowsiness results in inability to make the better perception and also decline in vehicle control abilities. A perfect system has been designed which is capable of predicting the level of alertness and ensures driver’s maximum performance. The real time EEG signal obtained from the brain which is acquired through Lab VIEW is fed to the ICAFNN, a fuzzy neural network model which is capable of self-adapting and structure self constructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of the system from the training data set. Object classification is also made through the images acquired from the driver’s face which provides additional information and makes the classification more accurate. All the process is designed using LabVIEW which supports parallel processing and enhances real time application. The combined results obtained from the ICAFNN and object classification is more precise than the existing methods.