Volume 3 • Issue 2 • 1000e118 J Ergonomics ISSN: 2165-7556 JER, an open access journal Open Access He, J Ergonomics 2013, 3:2 DOI: 10.4172/2165-7556.1000e118 Open Access Drowsiness disrupts work performance and increases the risks of accidents substantially, especially for performance-critical tasks, such as driving and piloting. Each year, 100,000 police-reported crashes are directly caused by driver drowsiness, which results in about 1,550 deaths, 71,000 injuries, and $12.5 billion fnancial losses, according to an estimation by the National Highway Trafc Safety Administration [1]. Te National Sleep Foundation estimated in 2002 that 51% of adult drivers had driven a vehicle while drowsy and 17% had fallen asleep behind the wheel. Pilot drowsiness is an important factor in pilot performance and aviation accidents [2]. Pilot drowsiness can reduce alertness and attention and cause longer response times [3]. Drowsiness is also concerning because people’s own judgment of their susceptibility to drowsiness is poor [4]. To mitigate the risks of drowsiness on driving and piloting, drowsiness detection technologies and drowsiness management programs are demanded. Tis article introduces existing and emerging technologies for drowsiness detection and drowsiness management strategies and calls for more research into the efects of drowsiness on task performance. Drowsy drivers and pilots manifest their fatigue states in various ways, such as heart rate, respiration rate, blood pressure, head and eye movement and task performance. For a drowsy driver or pilot, the delta and theta components of electroencephalography increase signifcantly [5]; heart rate decreases [6]; respiratory rate decreases; blood pressure increases [7,8]; blood oxygen concentration is lower (under 18%) [9]; blink rate and eye closure exceeding 1 second increase [4,5,10]; head nods more ofen [11]. Moreover, drowsy drivers and pilots perform poorly and make more errors [3,11]. Drowsiness can be detected using a variety of sensors, including electroencephalograph (EEG), electrocardiogram (ECG), polyphysiograph, accelerator sensors, and oculographical measurements. Te relative strengths of the EEG components, such as (θ+α)/β, (α+β)/θ, α/ β, (θ+ α)/ (α+β) and θ/ β, are commonly used to predict drowsiness [12]. Tree-axis accelerators can be used to detect head movements, such as head nods. Infrared cameras or smartphone cameras can be used to monitor oculographical measurements, such as eye blinks and PERCLOS (Percentage of Eye Closure). Diferent sensors have their own advantages and disadvantages. EEG is one of the most predictive and reliable techniques for drowsiness detection [13,14]. However, EEG sensors are ofen expensive, time- consuming to setup, and intrusive. Camera-based drowsiness detection ofen does not require users to wear a device, therefore less intrusive. However, it is hard to develop computer vision algorithms robust enough to detect faces and eyes of diferent skin colors, and under various weather and lighting conditions. Accelerator sensor is cheap but can only detect head movements. New advancements in mobile technologies and sensors make drowsiness detection more feasible and afordable in real-world tasks. For example, Dr. He and his coworkers used Android and iPhone smartphones to detect visual indicators of driver fatigue, such as head nods, head rotation, and eye blinks. Te increasing popularity of dry EEG sensors, such as Emotiv and Neurosky, makes it more afordable and convenient to collect EEG brain waves. Future research should consider predictability, reliability, afordability, and intrusiveness to detect drowsiness using various sensors or their combinations. Besides the above-mentioned technologies for drowsiness detection, some management strategies can also be used to combat drowsiness [15]. For example, drivers and pilots should take a break or should take turns afer working for a long period of time. However, temporary breaks from driving can only reduce time-on-task fatigue but not sleepiness [16]. Drivers and pilots can take cafeine, specifcally slow-release cafeine, if they have to continue their work and there is no opportunity to take a break [17]. Long-time drivers and pilots should be informed about their tendency toward drowsiness and provided drowsiness management techniques.Future research should evaluate the efectiveness of diferent counter measurements to reduce drowsiness and improve task performance. References 1. Rau PS (2005) Drowsy driver detection and warning system for commercial vehicle drivers: Field proportional test design, analysis, and progress. National Highway Traffc Safety Administration, USA, 05-0192: 1-7. 2. Goode JH (2003) Are pilots at risk of accidents due to fatigue? J Safety Res 34: 309-313. 3. Bourgeois-Bougrine S, Carbon P, Gounelle C, Mollard R, Coblentz A (2003) Perceived fatigue for short- and long-haul fights: a survey of 739 airline pilots. Aviat Space Environ Med 74: 1072-1077. 4. Verwey WB, Zaidel DM (2000) Predicting drowsiness accidents from personal attributes, eye blinks and ongoing driving behavior. Personality and Individual Differences 28: 123-142. 5. Lal SK, Craig A (2002) Driver fatigue: electroencephalography and psychological assessment. Psychophysiology 39: 313-321. 6. van den Berg J, Neely G, Wiklund U, Landström U (2005) Heart rate variability during sedentary work and sleep in normal and sleep-deprived states. Clin Physiol Funct Imaging 25: 51-57. 7. Snyder F, Hobson JA, Morrison DF, Goldfrank F (1964) Changes In Respiration, Heart Rate, And Systolic Blood Pressure In Human Sleep. J Appl Physiol 19: 417-422. 8. Tochikubo O, Ikeda A, Miyajima E, Ishii M (1996) Effects of insuffcient sleep on blood pressure monitored by a new multibiomedical recorder. Hypertension 27: 1318-1324. 9. Sung EJ, Min BC, Kim SC, Kim CJ (2005) Effects of oxygen concentrations on driver fatigue during simulated driving. Appl Ergon 36: 25-31. 10. Stern JA, Boyer D, Schroeder D (1994) Blink rate: a possible measure of fatigue. Hum Factors 36: 285-297. 11. He J, Fields B, Peng J, Cielocha S, Coltea, et al. (2013). Fatigue detection *Corresponding author: Jibo He, Department of Psychology, Wichita State University, USA, Tel.: (217) 417-3830; E-mail: jibo.he@wichita.edu Received August 05, 2013; Accepted August 07, 2013; Published August 10, 2013 Citation: He J (2013) Drowsiness Detection and Management. J Ergonomics 3: e118. doi:10.4172/2165-7556.1000e118 Copyright: © 2013 He J. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Drowsiness Detection and Management Jibo He* Department of Psychology, Wichita State University, USA Journal of Ergonomics J o u r n a l o f E r g o n o m i c s ISSN: 2165-7556 Editorial