Early detection of driver drowsiness by WPT and FLFNN models

This paper presents a method that can detect driver's drowsiness by using the wavelet packet transform (WPT) and functional link-based fuzzy neural network (FLFNN) models. Drowsy drivers have been reported to be vulnerable to car accidents. Early detection of drowsiness can help alert drivers or passengers to provide a safety drive on the road. For those old models or cars without equipped with advanced high technologies, there is a dire need to install sensor devices that can effectively detect drowsy status of drivers at an early stage. Photoplethysmography (PPG) is a non-invasive optical technique that measures relative blood volume changes in the blood vessels and has been universally used for research and physiological study. We develop such PPG sensor devices to be installed on the steering wheel to detect the physiological conditions (such as normal to drowsy) by using parameters extracted from the heart rate variability (HRV) obtained from PPG signal calculation. Experimental results revealed that the proposed model is effective in assessing the drowsy levels of drivers.

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