Real-time physiological and facial monitoring for safe driving

Abstract

This work is to develop an intelligent driver-assistance system which can perceive the physiological state of a driver to avoid fatigue driving. The proposed system includes a camera, a wireless ElectroCardioGram (ECG) sensor patch, and a computation platform. The camera in front of a driver is to catch a face image which is processed to obtain features of a mouth for identifying a yawn. The sensor patch records ECG signals which are computed to yield six Heart Rate Variability (HRV) parameters. Seven healthy subjects of 6 males and 1 female had individually driven a car, which was embedded with our system, for 3 hours at a well-known route, mostly in a freeway road. Based on the captured video and measured ECG signals, the correlations between the yawning frequency and six HRV parameters are investigated by using the regression method to discover that the ratio (LF/HF) of Low-Frequency (LF) spectrum power over High-Frequency (HF) spectrum power yields the relatively highest correlation. In order to effectively identity driver's fatigue, the variations of differential LF/HF are further characterized to attain two thresholds which are accompanied with yawning frequencies to build a fair detection mechanism. The practical road tests demonstrate that the proposed system is very feasible and easily adapted to different drivers.

Video pulse rate variability analysis in stationary and motion conditions

“…For example, the drowsiness in drivers is one the causes of traffic accidents all over the world. Therefore, some works [ 35 , 36 ] have proposed the measurement of HRV parameters in drivers during alert and drowsy or fatigued periods, in which some parameters showed significant differences between both states. Other studies have proposed the analysis by video to measure physiological parameters while driving [ 37 – 39 ] and other ones to detect cardiac arrhythmias [ 40 , 41 ].…”

dWatch

“…Moreover, dWatch has higher accuracy in detecting not only heart rate variability and steering wheel movement characteristics, but also driver yawning behavior through heart rate data. Chang et al [26] and Sun et al [31] use the camera to capture the driver's facial image to obtain the characteristics of the yawning mouth. The image is then combined with the wireless ECG sensor to develop a smart driving assistance system.…”

Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future

“…Beside the basal values, HRV has close links with fatigue and drowsiness detection (Li and Chung, 2013). Yu-Lung et al (2016) recorded ECG from wireless thoracic sensors and process the cardiac signal using HRV. Several parameters (e.g., low-frequency power spectrum over high-frequency power spectrum or LF/HF ratio) were closely correlated to several changes in drivers’ behavior, particularly with the frequency of yawning episodes.…”

“…Yu-Lung et al (2016) elaborated an intelligent driver assistance system including a camera in front of the driver for facial monitoring. Frequency of yawning was one of the main index predicting the occurrence of drowsiness (see also Sigari et al, 2014).…”