A Drowsiness Detection Architecture using Feature Extraction Methodology

AbstractDrowsiness is considered as a significant risk factor that contributes to large number of accidents. Therefore a method is developed for counteracting its effects and hence a very accurate classification of drowsiness is needed so as to categorize the different states of drowsiness and thus the person can be alerted at definite instants. The quality of extracted features is assessed on datasets collected from various subjects irrespective of age, sex and also the start and stop of the signal monitoring time of the subjects. An optimal Fuzzy Entropy Mutual Information(FEMI) methodology is implemented into Wavelet Packet Transform(WPT) decomposition so as to extract the features and the various instants of drowsiness of the subject is obtained. The Receiver Operating Characteristics (ROC) curve shows the classification accuracy of the SVM classifier in this proposed methodology