Pure and Organic CBD & and Hemp Products

Effective medicine provided by mother nature

  • Powerful relaxant

  • Strong painkiller

  • Stress reduction
  • Energy booster

Why CBD?

More and more renowned scientists worldwide publish their researches on the favorable impact of CBD on the human body. Not only does this natural compound deal with physical symptoms, but also it helps with emotional disorders. Distinctly positive results with no side effects make CBD products nothing but a phenomenal success.

This organic product helps cope with:

  • Tight muscles
  • Joint pain
  • Stress and anxiety
  • Depression
  • Sleep disorder

Range of Products

We have created a range of products so you can pick the most convenient ones depending on your needs and likes.

CBD Capsules Morning/Day/Night:

CBD Capsules

These capsules increase the energy level as you fight stress and sleep disorder. Only 1-2 capsules every day with your supplements will help you address fatigue and anxiety and improve your overall state of health.

Order Now

CBD Tincture

CBD Tincture

No more muscle tension, joints inflammation and backache with this easy-to-use dropper. Combined with coconut oil, CBD Tincture purifies the body and relieves pain. And the bottle is of such a convenient size that you can always take it with you.

Order Now

Pure CBD Freeze

Pure CBD Freeze

Even the most excruciating pain can be dealt with the help of this effective natural CBD-freeze. Once applied on the skin, this product will localize the pain without ever getting into the bloodstream.

Order Now

Pure CBD Lotion

Pure CBD Lotion

This lotion offers you multiple advantages. First, it moisturizes the skin to make elastic. And second, it takes care of the inflammation and pain. Coconut oil and Shia butter is extremely beneficial for the health and beauty of your skin.

Order Now

Aurora Cannabis Introduces CBD Oil Cartridges for Vape Pens

Drowsiness 2.



  • Drowsiness 2.
  • Why Does Type 2 Diabetes Make You Feel So Tired?
  • 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.

    In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system.

    In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed.

    We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

    According to available statistical data, over 1. The German Road Safety Council DVR claims that one in four highway traffic fatalities are a result of momentary driver drowsiness [ 4 ]. These statistics suggest that driver drowsiness is one of the main causes of road accidents.

    A driver who falls asleep at the wheel loses control of the vehicle, an action which often results in a crash with either another vehicle or stationary objects. In order to prevent these devastating accidents, the state of drowsiness of the driver should be monitored.

    The following measures have been used widely for monitoring drowsiness:. Other than these three, researchers have also used subjective measures where drivers are asked to rate their level of drowsiness either verbally or through a questionnaire. The intensity of drowsiness is determined based on the rating [ 15 , 16 ]. These methods have been studied in detail and the advantages and disadvantages of each have been discussed. However, in order to develop an efficient drowsiness detection system, the strengths of the various measures should be combined into a hybrid system.

    The organization of this paper is as follows: Section 2 discusses driver drowsiness in detail. Section 3 describes the simulated environment for drowsiness manipulation and Section 4 analyses the various methods of drowsiness manipulation for study purposes. Section 5 describes the different methods that have been studied for detecting driver drowsiness, Section 6 discusses on driving conditions and hybrid measures, and Section 7 concludes by presenting the benefit of fusing various measures to develop an efficient system.

    The second stage, NREM, can be subdivided into the following three stages [ 17 ]:. In order to analyze driver drowsiness, researchers have mostly studied Stage I, which is the drowsiness phase.

    The crashes that occur due to driver drowsiness have a number of characteristics [ 18 ]:. 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 ]:. Statistics derived using these criteria cannot account fully for accidents caused by drowsiness because of the complexity involved; therefore, accidents that can be attributed to driver drowsiness may be more devastating than the statistics reveal.

    Hence, in order to avoid these types of accidents, it is necessary to derive effective measures to detect driver drowsiness and alert the driver. It is not safe and ethical to make a drowsy driver drive on road. Hence, researchers have used simulated environments to carry out their experiments. Experimental control, efficiency, low cost, safety, and ease of data collection are the main advantages of using simulators [ 19 , 20 ]. The driving simulators can be broadly classified as: Reproduced with permission from Mini Sim: One important limitation of using driving simulators is that the drivers do not perceive any risk.

    The awareness of being immersed in a simulated environment might give a behavior which is different than that on real road [ 22 ]. However, researchers have validated that driving simulators can create driving environment that are relatively similar to road experiments [ 23 — 25 ]. Researchers have observed behavioral [ 20 , 23 ], vehicle based [ 6 ] and physiological [ 24 ] similarity between simulated and on road experiments. One of the challenges in developing an efficient drowsiness detection system is how to obtain proper drowsiness data.

    Due to safety reasons, drowsiness cannot be manipulated in a real environment; thus, the drowsiness detection system has to be developed and tested in a laboratory setting. However, in a laboratory setting, the most reliable and informative data that pertains to driver drowsiness relies only on the way in which the driver falls into the drowsy state.

    Driver drowsiness mainly depends on: In some research experiments, the subjects were fully deprived of sleep, whereas they were only partially deprived of sleep in others [ 28 ]. In addition, some researchers recruited night shift workers as their subjects; in these cases, the subjects were totally deprived of sleep because the experiments were conducted in the morning [ 26 , 28 ].

    In certain experiments, researchers partially deprived the subjects of sleep by allowing them to sleep for less than 6 h [ 14 ]. They observed that, even in the case of partial sleep deprivation, the subjects tend to get drowsy after some time. Hence, the quality of the last sleep is an important criteria that influences drowsiness [ 29 ]. The performance of the driver deteriorates when physiological activity diminishes [ 30 ].

    A circadian rhythm is used to refer to any biological variations or rhythms that occur in a cycle of approximately 24 h. These rhythms are self-sustaining i. 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: 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 Researchers have asked subjects to drive between 2: The duration of the driving task also plays a major role in influencing drowsiness.

    Researchers have also inferred that prolonged driving on a monotonous environment stimulates drowsiness. In fact, it has been observed that the subjects can become drowsy within 20 to 25 min of driving [ 33 ]. This last finding, reported by Philip et al.

    In addition, researchers have found that drowsiness-related crashes are more probable in a monotonous environment than in a stimulating environment. Therefore, there is a very high probability that a partially sleep-deprived driver will become drowsy when driving in a monotonous environment at a constant speed for three hours during a time when their circadian rhythm is low. This should be taken into consideration when designing an experiment relating to recording driver drowsiness.

    Researchers have used various methods to measure driver drowsiness. This section provides a review of the four most widely-used methods, among which the first method is measured either verbally or through questionnaire and the remaining three by means of various sensors. The most commonly used drowsiness scale is the Karolinska Sleepiness Scale KSS , a nine-point scale that has verbal anchors for each step, as shown in Table 1 [ 32 ].

    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. A number of diagnostic tests, including the Epworth Sleepiness Scale , are available to help ascertain the seriousness and likely causes of abnormal somnolence.

    Somnolence is a symptom , so the treatment will depend on its cause. If the cause is the behavior and life choices of the patient like working long hours, smoking, mental state , it may help to get plenty of rest and get rid of distractions. From Wikipedia, the free encyclopedia. Circadian rhythm sleep disorder. The dictionary definition of drowsiness at Wiktionary Chronic fatigue syndrome Decision fatigue Fibromyalgia Insomnia Hypersomnia Dyssomnia Fatigue physical Postprandial somnolence Restless legs syndrome Periodic limb movement Hypnopompic Hypnagogia.

    American Journal of Physiology. Regulatory, Integrative and Comparative Physiology. Brain, Behavior, and Immunity. Cochrane Database of Systematic Reviews Journal of Sleep Research. Carotid sinus syncope Heat syncope Vasovagal episode. Anxiety Irritability Hostility Suicidal ideation. Hypersomnia Insomnia Kleine—Levin syndrome Narcolepsy Sleep apnea Central hypoventilation syndrome Obesity hypoventilation syndrome Sleep state misperception.

    Advanced sleep phase disorder Delayed sleep phase disorder Irregular sleep—wake rhythm Jet lag Nonhour sleep—wake disorder Shift work sleep disorder. Catathrenia Night terror Rapid eye movement sleep behavior disorder Sleepwalking Somniloquy. Bruxism Cyclic alternating pattern Night eating syndrome Nocturia Nocturnal myoclonus. Retrieved from " https:

    Why Does Type 2 Diabetes Make You Feel So Tired?

    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.

    Add Comment