Non-obtrusive physiological sensors to estimate the drowsiness of drivers are expected to become feasible in the near future [ 70 , 75 ]. During these time frames, the circadian rhythm shows higher chance of getting drowsy and drivers are three times more likely to fall asleep at these times than at Physiological responses to simulated and on-road driving. One of the challenges in developing an efficient drowsiness detection system is how to obtain proper drowsiness data. Discussion The various measures of driver drowsiness reviewed in this work are based purely on the level of drowsiness induced in the subject, which, in turn, depends on the time of day, duration of the task and the time that has elapsed since the last sleep. Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system. The following measures have been used widely for monitoring drowsiness:

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Detecting Driver Drowsiness Based on Sensors: A Review

Multiscale dynamic features based driver fatigue detection. In relation to these characteristics, the Southwest England and the Midlands Police databases use the following criteria to identify accidents that are caused by drowsiness [ 5 ]:. Reproduced with permission from Mini Sim: Physiological responses to simulated and on-road driving.

Recent statistics from countries such as the United Kingdom, the United States, Israel, Finland, and France indicate that an increased number of vehicle accidents caused by driver drowsiness occurred during the peak drowsiness periods of 2: Open in a separate window.

Duration of eyelid closure, No. Real-time physiological sleep sensing and alerting system for vision monitoring of vehicle driver for non-intrusive drowsiness detection.

The second stage, NREM, can be subdivided into the following three stages [ 17 ]: Mostly, image is acquired using simple CCD or web camera during day [ 55 ] and IR camera during night [ 56 ] at around 30 fps. Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.


Physiological measures—The correlation between physiological signals electrocardiogram ECGelectromyogram EMGsleep sensing and alerting system for EoG and electroencephalogram EEG and driver drowsiness has been studied by many researchers [ 10 — 14 ]. These statistics suggest that driver drowsiness is one of the main causes of road accidents.

Hence, in order to eliminate noise, various preprocessing techniques, such as low pass filter, digital differentiators, have been used Table 2. A decrease in the power changes in the alpha frequency band and an increase in the theta frequency band indicates drowsiness.

Advantages and limitations of various measures. Real-time system for monitoring driver vigilance.

In order to overcome this limitation, some researchers have used active illumination utilizing an infrared Light Emitting Diode LED [ 43 ]. Steering Wheel Movement SWM is measured using steering angle sensor and it is a widely used sleep sensing and alerting system for measure for detecting the level of driver drowsiness [ 323336 ].

This an is an open access article distributed under the terms and conditions of the Creative Commons Attribution license http: However, the two most commonly used vehicle-based measures are the steering wheel movement and the standard deviation of lane position. The advantages of physiological measures and the increasing availability of non-intrusive measurement equipment make it beneficial to combine physiological signals with behavioral and vehicle-based measures.

However, the subjective rating does not fully coincide with vehicle-based, sleep sensing and alerting system for and behavioral measures.

Sensing Driver Sleepiness | HowStuffWorks

EEG signals require a number of electrodes to be placed on the scalp and the electrodes used for measuring Sleep sensing and alerting system for signals are placed near the eye which can hinder driving. In the above experiment by performing correlation analysis on a subject to subject basis significant difference is noted.


Behavioral measures are sleep sensing and alerting system for efficient way to detect drowsiness and some real-time products have been developed [ 74 ]. Likewise, as most researchers conducted their experiments in simulated environment they achieved a higher success rate. Cascaded Classifiers Algorithm detects face and Diamond searching lgorithm to trace the face. In fact, it has been observed that the subjects can become drowsy within 20 to 25 min of driving [ 33 ].

Consequently, many experiments have been conducted in simulated environments and the results of the experiments are then elaborately studied. List of previous works on driver drowsiness detection using physiological signals. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens.

You can easily adjust the angle that triggers the alarm. Most experiments using behavioral measures are conducted in a simulated environment and the results indicate that it is a reliable method to detect drowsiness.

Yawning Eye closure Eye blink Head pose. In summary, many studies have determined that vehicle-based measures are a poor predictor of performance error risk due to drowsiness.

Although hybrid systems using different sensors have not been tested in a real environment, sustem would be interesting to investigate the ability to detect drowsiness using a combination of physiological signals with other measurements.