Principal Investigator(s):
Xun Yu, Former Associate Professor, UMD-Mechanical & Industrial Eng
Project summary:
This project is an extension of a Northland Advanced Transportation System Research Laboratory project titled Real-time Nonintrusive Detection of Driver Drowsiness, which aimed to develop a real-time, nonintrusive driver drowsiness detection system to reduce crashes caused by driver drowsiness. In previous research, nonintrusive sensors for measuring a driver?s heart beat were developed for and implemented on a vehicle's steering wheel. Heart rate variability (HRV) was analyzed from the heart beat pulse signals for detecting driver drowsiness, which yielded promising results. However, one of the major issues with the previous system is that it used only one parameter, the low-frequency (LF)/high-frequency (HF) ratio of HRV, to access the driver's status. This method has a relatively high variability and different patterns for different drivers. In this project, the researchers used multiple parameters for drowsiness detection, including the LF/HF ratio, steering wheel motion variability, and electroencephalography (EEG) parameters, and correlations between these parameters were analyzed.