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Drowsiness 2.



  • Drowsiness 2.
  • Drowsy Driving: Asleep at the Wheel
  • 1. Introduction
  • Feeling abnormally sleepy or tired during the day is commonly known as drowsiness. Drowsiness may lead to additional symptoms, such as forgetfulness or Common head injuries include concussions, · READ MORE · 2. Drowsiness refers to feeling abnormally sleepy during the day. People who are drowsy may fall asleep in inappropriate situations or at. The problem of daytime sleepiness usually starts at night. 2. Keep distractions out of bed. “Reserve your bed for sleep and sex,” says Avelino.

    Drowsiness 2.

    The risk, danger, and often tragic results of drowsy driving are alarming. Drowsy driving is the dangerous combination of driving and sleepiness or fatigue.

    This usually happens when a driver has not slept enough, but it can also happen due to untreated sleep disorders, medications, drinking alcohol, or shift work. No one knows the exact moment when sleep comes over their body. Researchers estimate that more than 70 million Americans suffer from a sleep disorder Institute of Medicine, If you experience any of these warnings signs, pull over to rest or change drivers.

    Simply turning up the radio or opening the window are not effective ways to keep you alert. For more warning signs visit American Academy of Sleep Medicine. Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content.

    Get Email Updates To receive email updates about this page, enter your email address: Some researchers compared the self-determined KSS, which was recorded every 2 min during the driving task, with the variation of lane position VLP and found that these measures were not in agreement [ 35 ]. Researchers have determined that major lane departures, high eye blink duration and drowsiness-related physiological signals are prevalent for KSS ratings between 5 and 9 [ 26 ].

    However, the subjective rating does not fully coincide with vehicle-based, physiological and behavioral measures. Because the level of drowsiness is measured approximately every 5 min, sudden variations cannot be detected using subjective measures.

    Another limitation to using subjective ratings is that the self-introspection alerts the driver, thereby reducing their drowsiness level.

    In addition, it is difficult to obtain drowsiness feedback from a driver in a real driving situation. Therefore, while subjective ratings are useful in determining drowsiness in a simulated environment, the remaining measures may be better suited for the detection of drowsiness in a real environment. Another method to measure driver drowsiness involves vehicle-based measurements. In most cases, these measurements are determined in a simulated environment by placing sensors on various vehicle components, including the steering wheel and the acceleration pedal; the signals sent by the sensors are then analyzed to determine the level of drowsiness.

    Some researchers found that sleep deprivation can result in a larger variability in the driving speed [ 36 ]. However, the two most commonly used vehicle-based measures are the steering wheel movement and the standard deviation of lane position. Steering Wheel Movement SWM is measured using steering angle sensor and it is a widely used vehicle-based measure for detecting the level of driver drowsiness [ 32 , 33 , 36 ].

    When drowsy, the number of micro-corrections on the steering wheel reduces compared to normal driving [ 37 ]. Fairclough and Graham found that sleep deprived drivers made fewer steering wheel reversals than normal drivers [ 36 ]. To eliminate the effect of lane changes, the researchers considered only small steering wheel movements between 0. Hence, based on small SWMs, it is possible to determine the drowsiness state of the driver and thus provide an alert if needed.

    In a simulated environment, light side winds that pushed the car to the right side of the road were added along a curved road in order to create variations in the lateral position and force the drivers to make corrective SWMs [ 33 ].

    Car companies, such as Nissan and Renault, have adopted SWMs but it works in very limited situations [ 38 ]. This is because they can function reliably only at particular environments and are too dependent on the geometric characteristics of the road and to a lesser extent on the kinetic characteristics of the vehicle [ 38 ].

    In a simulated environment, the software itself gives the SDLP and in case of field experiments the position of lane is tracked using an external camera. In the above experiment by performing correlation analysis on a subject to subject basis significant difference is noted. Another limitation of SDLP is that it is purely dependent on external factors like road marking, climatic and lighting conditions. In summary, many studies have determined that vehicle-based measures are a poor predictor of performance error risk due to drowsiness.

    Moreover, vehicular-based metrics are not specific to drowsiness. SDLP can also be caused by any type of impaired driving, including driving under the influence of alcohol or other drugs, especially depressants [ 39 — 41 ].

    A drowsy person displays a number of characteristic facial movements, including rapid and constant blinking, nodding or swinging their head, and frequent yawning [ 7 ].

    Computerized, non-intrusive, behavioral approaches are widely used for determining the drowsiness level of drivers by measuring their abnormal behaviors [ 42 ]. Most of the published studies on using behavioral approaches to determine drowsiness, focus on blinking [ 43 — 45 ].

    This measurement has been found to be a reliable measure to predict drowsiness [ 46 ] and has been used in commercial products such as Seeing Machines [ 49 ] and Lexus [ 50 ]. Some researchers used multiple facial actions, including inner brow rise, outer brow rise, lip stretch, jaw drop and eye blink, to detect drowsiness [ 9 , 42 ].

    However, research on using other behavioral measures, such as yawning [ 51 ] and head or eye position orientation [ 52 , 53 ], to determine the level of drowsiness is ongoing Table 2. The main limitation of using a vision-based approach is lighting.

    Normal cameras do not perform well at night [ 43 ]. In order to overcome this limitation, some researchers have used active illumination utilizing an infrared Light Emitting Diode LED [ 43 ].

    However, although these work fairly well at night, LEDs are considered less robust during the day [ 54 ]. In addition, most of the methods have been tested on data obtained from drivers mimicking drowsy behavior rather than on real video data in which the driver gets naturally drowsy. Mostly, image is acquired using simple CCD or web camera during day [ 55 ] and IR camera during night [ 56 ] at around 30 fps. After capturing the video, some techniques, including Connected Component Analysis, Cascade of Classifiers or Hough Transform, Gabor Filter, Haar Algorithm are applied to detect the face, eye or mouth [ 8 , 42 , 44 , 56 ].

    After localizing the specific region of interest within the image, features such as PERCLOS, yawning frequency and head angle, are extracted using an efficient feature extraction technique, such as Wavelet Decomposition, Gabor Wavelets, Discrete Wavelet Transform or Condensation Algorithm [ 7 , 42 , 44 , 56 ].

    The behavior is then analyzed and classified as either normal, slightly drowsy, highly drowsy through the use of classification methods such as support vector machine, fuzzy classifier, neural classifier and linear discriminant analysis [ 7 , 42 — 44 ]. However, it has been found that the rate of detecting the correct feature, or the percentage of success among a number of detection attempts, varies depending on the application and number of classes. Likewise, as most researchers conducted their experiments in simulated environment they achieved a higher success rate.

    The positive detection rate decreased significantly when the experiment was carried out in a real environment [ 15 ]. Another limitation of behavioral measure was brought out in an experiment conducted by Golz et al.

    They evaluated various drowsiness monitoring commercial products, and observed that driver state cannot be correlated to driving performance and vehicle status based on behavioral measures alone [ 57 ]. As drivers become drowsy, their head begins to sway and the vehicle may wander away from the center of the lane.

    The previously described vehicle-based and vision based measures become apparent only after the driver starts to sleep, which is often too late to prevent an accident. However, physiological signals start to change in earlier stages of drowsiness.

    Hence, physiological signals are more suitable to detect drowsiness with few false positives; making it possible to alert a drowsy driver in a timely manner and thereby prevent many road accidents. Many researchers have considered the following physiological signals to detect drowsiness: Some researchers have used the EoG signal to identify driver drowsiness through eye movements [ 12 , 28 , 61 ].

    The electric potential difference between the cornea and the retina generates an electrical field that reflects the orientation of the eyes; this electrical field is the measured EoG signal. Researchers have investigated horizontal eye movement by placing a disposable Ag-Cl electrode on the outer corner of each eye and a third electrode at the center of the forehead for reference [ 28 ]. The electrodes were placed as specified so that the parameters - Rapid eye movements REM and Slow Eye Movements SEM which occur when a subject is awake and drowsy respectively, can be detected easily [ 30 ].

    The heart rate HR also varies significantly between the different stages of drowsiness, such as alertness and fatigue [ 13 , 63 ]. Therefore, heart rate, which can be easily determined by the ECG signal, can also be used to detect drowsiness. The Electroencephalogram EEG is the physiological signal most commonly used to measure drowsiness.

    The EEG signal has various frequency bands, including the delta band 0. A decrease in the power changes in the alpha frequency band and an increase in the theta frequency band indicates drowsiness.

    The measurement of raw physiological signals is always prone to noise and artifacts due to the movement that is involved with driving. Hence, in order to eliminate noise, various preprocessing techniques, such as low pass filter, digital differentiators, have been used Table 2. In general, an effective digital filtering technique would remove the unwanted artifacts in an optimal manner [ 64 ]. The reliability and accuracy of driver drowsiness detection by using physiological signals is very high compared to other methods.

    However, the intrusive nature of measuring physiological signals remains an issue to be addressed. To overcome this, researchers have used wireless devices to measure physiological signals in a less intrusive manner by placing the electrodes on the body and obtaining signals using wireless technologies like Zigbee [ 65 ], Bluetooth [ 66 ].

    The signals obtained were then processed in android based smart phone devices [ 70 , 71 ] and the driver was alerted on time. The accuracy of a non-intrusive system is relatively less due to movement artifacts and errors that occur due to improper electrode contact.

    However, researchers are considering to use this because of its user friendliness. In recent years, experiments are conducted to validate non-intrusive systems [ 68 , 69 ]. The advantages and disadvantages of the different type of measures are summarized in Table 4.

    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. However, when developing a better drowsiness detection system, several other issues need to be addressed; the two most important ones are discussed below. It is not advisable to force a drowsy driver to drive on roads.

    Consequently, many experiments have been conducted in simulated environments and the results of the experiments are then elaborately studied. The subjective self-assessment of drowsiness can only be obtained from subjects in simulated environments. In real conditions, it is unfeasible to obtain this information without significantly distracting the driver from their primary task.

    Some researchers have conducted experiments to confirm the validity of simulated driving environments. For example, Blana et al. This finding implies that real-road drivers feel less safe at higher speeds and, as a result, increase their lateral distance. The drivers in a simulated environment, however, did not appear to perceive this risk [ 72 ].

    Most experiments using behavioral measures are conducted in a simulated environment and the results indicate that it is a reliable method to detect drowsiness. However, in real driving conditions, the results might be significantly different because a moving vehicle can present challenges such as variations in lighting, change in background and vibration noise, not to mention the use of sunglasses, caps, etc. This result can be interpreted as an indication of increased effort, which seems reasonable given the higher actual risk in real traffic [ 73 ].

    Hence, while developing a drowsiness detection system, the simulated environment should be as close to a replica of the real environment as possible. Each method used for detecting drowsiness has its own advantages and limitations. Vehicle-based measures are useful in measuring drowsiness when a lack of vigilance affects vehicle control or deviation.

    However, in some cases, there was no impact on vehicle-based parameters when the driver was drowsy [ 26 ], which makes a vehicle-based drowsiness detection system unreliable. Behavioral measures are an efficient way to detect drowsiness and some real-time products have been developed [ 74 ]. However, when evaluating the available real-time detection systems, Lawrence et al.

    Physiological measures are reliable and accurate because they provide the true internal state of the driver; however, their intrusive nature has to be resolved. Among all physiological parameters investigated, ECG can be measured in a less intrusive manner.

    EEG signals require a number of electrodes to be placed on the scalp and the electrodes used for measuring EoG signals are placed near the eye which can hinder driving. Somnolence alternatively " sleepiness " or " drowsiness " is a state of strong desire for sleep , or sleeping for unusually long periods compare hypersomnia.

    It has distinct meanings and causes. It can refer to the usual state preceding falling asleep, [1] the condition of being in a drowsy state due to circadian rhythm disorders, or a symptom of other health problems. It can be accompanied by lethargy , weakness, and lack of mental agility. Somnolence is often viewed as a symptom rather than a disorder by itself. However, the concept of somnolence recurring at certain times for certain reasons constitutes various disorders, such as excessive daytime sleepiness , shift work sleep disorder , and others; and there are medical codes for somnolence as viewed as a disorder.

    Sleepiness can be dangerous when performing tasks that require constant concentration, such as driving a vehicle. When a person is sufficiently fatigued , microsleeps may be experienced. In individuals deprived of sleep, somnolence may spontaneously dissipate for short periods of time; this phenomenon is the second wind , and results from the normal cycling of the circadian rhythm interfering with the processes the body carries out to prepare itself to rest. Circadian rhythm "biological clock" disorders are a common cause of drowsiness as are a number of other conditions such as sleep apnea, insomnia, and narcolepsy.

    The former type is, for example, shift work sleep disorder , which affects people who work nights or rotating shifts. The intrinsic types include:

    Drowsy Driving: Asleep at the Wheel

    2. Take a Nap to Take the Edge Off Sleepiness. There are two things to remember about naps: Don't take more than one and don't take it too. Somnolence (alternatively "sleepiness" or "drowsiness") is a state of strong desire for sleep, drowsiness. 2 Severity; 3 Treatment; 4 See also; 5 References . Stage II: light sleep. Stages III: deep sleep. In order to analyze driver drowsiness, researchers have mostly studied Stage I.

    1. Introduction



    2. Take a Nap to Take the Edge Off Sleepiness. There are two things to remember about naps: Don't take more than one and don't take it too.


    Somnolence (alternatively "sleepiness" or "drowsiness") is a state of strong desire for sleep, drowsiness. 2 Severity; 3 Treatment; 4 See also; 5 References .


    Stage II: light sleep. Stages III: deep sleep. In order to analyze driver drowsiness, researchers have mostly studied Stage I.


    Secondly, the rate and duration of blinking in times and information of drowsiness were perfect. As shown in Fig. 2, PERCLOS and blinking rate had upward and.


    The most common causes of excessive daytime sleepiness are sleep and shift workers,2 but assessment of its true prevalence is difficult.


    Clare Anderson, PhD1,2,3,*, Suzanne Ftouni, PhD3, Joseph M. Ronda, .. Figure 2. Self-reported sleepiness and objective drowsiness while.

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