Abstract
The detection of sleep is important because it contributes to most road accidents especially high levels of deep sleep while driving. Sleep detection is based on electrooculogram (EoG) signal as sleep causes various changes to this signal. Drivers travelling for long hours, especially those working under transportation field are more likely to sleep in the middle of their journey. In order to avoid this situation, drivers are aided with a system which is capable of monitoring the drivers’ condition depending on communication between the driving simulator and the subject EoG signal as many sleep detection devices are dependent upon eye behavior and movement in addition to pupil size and eye closure for certain periods. Therefore, to solve the problem of detecting sleep while driving, this work extracted different features from the EoG signal precisely from its frequency range (0–25[Formula: see text]Hz) and (25–37.5[Formula: see text]Hz) by discrete wavelet transform technique. In this research, 15 subjects have been set in a driving environment for more than 1[Formula: see text]h for collecting the sleep EoG signal data by low power sensors. The EoG signal is recorded using Cobra3 Data acquisition set and few features (minimum, maximum, mean, standard deviation (SD), mode, energy, median and variance) are extracted using discrete wavelet transform. These features have been used to classify three classes (sleep 0, sleep 0, sleep 1) using support vector machine (SVM). This classifier depends upon the fusion of the above features to get an accuracy of 78% for high-level sleep detection based on db4 wavelet.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Biomedical Engineering: Applications, Basis and Communications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.