EEG Signal Classification and Drowsiness Detection in Driver
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
traffic accidents are increasing due to driver’s consumption of alcohol or due to drowsiness. Breathalyzers are used for the purpose of alcohol detection, these device measure the amount of alcohol rather than loss of function caused by alcohol. In this paper people were tested and determined whether they were alcoholic or not with the help of eeg data. Based on the features extracted from wavelet transform, we can classify using svm the given eeg signal contains the alcoholic content or not. Along with the alcoholic detection the condition of the driver was also monitored. I.e., the driver fatigue monitoring system using image processing algorithms such as viola-jones detection, clahe, haar wavelet. We can recognize the fatigue condition and drowsiness condition by processing on the captured images of driver’s face. Depending on the eye’s position or yawning of the driver we can predict that he is in drowsy state or not.
Authors and Affiliations
S Gulnaz, B. S. Anjan Kumar, Dr. H. N. Suresh
Keywords
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