What is Characteristic Path Length

The present chapter deals with the development of a robust real-time embedded system which can detect the level of drowsiness in automotive and locomotive drivers based on ocular images and speech signals of the driver. The system has been cross-validated using Electroencephalogram (EEG) as well as Psychomotor response tests. A ratio based on eyelid closure rates called PERcentage of eyelid CLOSure (PERCLOS) using Principal Component Analysis (PCA) and Support Vector Machine (SVM) is employed to determine the state of drowsiness. Besides, the voiced-to-unvoiced speech ratio has also been used. Source localization and synchronization of EEG signals have been employed for detection of various brain stages during various stages of fatigue and cross-validating the algorithms based in image and speech data. The synchronization has been represented in terms of a complex network and the parameters of the network have been used to trace the change in fatigue of sleep-deprived subjects. In addition, subjective feedback has also been obtained.