Review on the application of physiological and biomechanical measurement methods in driving fatigue detection
Previous studies have identified driving fatigue as the main cause of road traffic accidents, therefore, the aim of this literature review is to explore the characteristics of driving fatigue both physically and mentally as well as to explore the technology available to measure the process of fatigue physiologically. We performed e-searching in the field of fatigue detection methods through keywords tracking. The instruments studied have their own strength and weakness, and some are intrusive while the others are non-intrusive. The accuracy and stability of measurements are also varied between those instruments. In order to create more reliable fatigue detection methods, it is necessary to involve more instruments with an inter-disciplinary approach. Our intention is to make this study as a stepping stone for a more comprehensive in-vehicle real-time man-machine interaction study. Such study will not only be useful to prevent traffic accidents but also to bridge man and machine communication in the vehicle control along with developing newer technology in the field of vehicle automation.
B. Z. Khan, “Knowledge, Human Capital, and Economic Development: Evidence From the British Industrial Revolution, 1750-1930,” 2015.
G. W. Jones, Contemporary Demographic Transformations in China, India and Indonesia. Cham: Springer International Publishing, 2016.
“Prioritas pembangunan nasional.” [Online]. Available: http://www.bappenas.go.id/files/8213/5027/5942/bab-i-prioritas-pembangunan-nasional.pdf. [Accessed: 25-Feb-2016].
World Health Organization, “Global Status Report on Road Safety,” Geneva, 2015.
K. T. Chui et al., “Electrocardiogram based classifier for driver drowsiness detection,” in Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, 2015, pp. 600–603.
“Kecelakaan Lalu Lintas Menjadi Pembunuh Terbesar Ketiga | BADAN INTELIJEN NEGARA REPUBLIK INDONESIA.” [Online]. Available: http://www.bin.go.id/awas/detil/197/4/21/03/2013/kecelakaan-lalu-lintas-pembunuh-terbesar-ketiga. [Accessed: 25-Feb-2016].
L. L. Di Stasi et al., “Effects of driving time on microsaccadic dynamics,” Exp. Brain Res., vol. 233, no. 2, pp. 599–605, 2015.
M. Rost et al., “Comparing Contribution of Algorithm Based Physiological Indicators for Characterisation of Driver Drowsiness,” J. Med. Bioeng., vol. 4, no. 5, pp. 391–398, 2015.
X. Fan et al., “Electroencephalogram assessment of mental fatigue in visual search,” Biomed. Mater. Eng., vol. 26, no. 37, pp. 1455–1463, 2015.
E. Michail et al., “EEG and HRV markers of sleepiness and loss of control during car driving,” in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, 2008, pp. 2566–2569.
W. Sun et al., “Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection,” Math. Probl. Eng., pp. 1–13, 2015.
J. Vicente et al., “Drowsiness detection using heart rate variability,” Med. Biol. Eng. Comput., 2016.
K. Lienhard et al., “sEMG during whole-body vibration contains motion artifacts and reflex activity,” J. Sport. Sci. Med., vol. 14, no. 1, pp. 54–61, 2014.
J. H. Ju et al., “Real-Time Driver ’ s Biological Signal Monitoring System,” Sensors Mater., vol. 27, no. 1, pp. 51–59, 2015.
T. Selvan N and R. Shanmugalakshmi, “A review on driver fatigue detection system,” Int. J. Innov. Sci. Appl. Eng. Res., vol. 13, no. 4, pp. 29–33, 2015.
E. Rashedi and M. A. Nussbaum, “A review of occupationally-relevant models of localised muscle fatigue,” Int. J. Hum. Factors Model. Simul., vol. 5, no. 1, pp. 61–80, 2015.
C. D. Marlin, “The Physiology of Fatigue in Horses During Exercise,” Time, 2007.
D.-H. Kang et al., “Effects of low-frequency electrical stimulation on cumulative fatigue and muscle tone of the erector spinae.,” J. Phys. Ther. Sci., vol. 27, no. 1, pp. 105–8, 2015.
N. R. Carlson, Physiology of behavior. New York: Allyn&Bacon, 2010.
S. B. N. Thompson, “Yawning, fatigue, and cortisol: Expanding the Thompson Cortisol Hypothesis,” Med. Hypotheses, vol. 83, no. 4, pp. 494–496, 2014.
P. N. Lahange et al., “Driver ’ s Face Monitoring System for Det ecting Hypo-Vigilance : A Review,” Int. J. Res., vol. 2, no. 4, pp. 553–559, 2015.
J. F. May and C. L. Baldwin, “Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies,” Transp. Res. Part F Traffic Psychol. Behav., vol. 12, no. 3, pp. 218–224, 2009.
L. J. Trejo et al., “EEG-Based Estimation and Classification of Mental Fatigue,” Psychology, vol. 06, no. 05, pp. 572–589, 2015.
M. Sato, “The Development of Conceptual Framework in Physiological Anthropology,” J. Physiol. Anthropol. Appl. Human Sci., vol. 24, no. 4, pp. 289–295, 2005.
K. Iwanaga, “The biological aspects of physiological anthropology with reference to its five keywords.,” J. Physiol. Anthropol. Appl. Human Sci., vol. 24, pp. 231–235, 2005.
A. Schmidt, “Biosignals in human-computer interaction,” interactions, vol. 23, no. 1, pp. 76–79, Dec. 2015.
D. A. Winter, Biomechanics and Motor Control of Human Movement. John Wiley & Sons, 2009.
B. Rosenhahn et al., “Human Motion - Understanding, Modelling, Capture, and Animation,” Image (Rochester, N.Y.), p. 635, 2008.
D. Papaioannou et al., “Literature searching for social science systematic reviews: Consideration of a range of search techniques,” Health Info. Libr. J., vol. 27, no. 2, pp. 114–122, 2010.
M. Boeker et al., “Literature search methodology for systematic reviews: Conventional and natural language processing enabled methods are complementary (Letter commenting on: J Clin Epidemiol. 2015;68:191-9),” J. Clin. Epidemiol., vol. 69, pp. 255–257, 2016.
A. Skene, “Writing a Literature Review,” Toronto, 2012.
University of North Carolina, “Literature reviews.,” Chapel Hill, 2014.
T. A. Dingus et al., “Driver crash risk factors and prevalence evaluation using naturalistic driving data,” pp. 1–7, 2016.
H. J. Oh et al., “Effects of Superimposition of a Head-Up Display on Driving Performance and Glance Behavior in the Elderly,” Int. J. Hum. Comput. Interact., vol. 32, no. 2, pp. 143–154, Oct. 2015.
F. Naujoks et al., “Secondary task engagement and vehicle automation – Comparing the effects of different automation levels in an on-road experiment,” Transp. Res. Part F Traffic Psychol. Behav., vol. 38, pp. 67–82, Apr. 2016.
K. M. Gruevski et al., “Lumbar postures, seat interface pressures and discomfort responses to a novel thoracic support for police officers during prolonged simulated driving exposures,” Appl. Ergon., vol. 52, pp. 160–168, 2016.
X. Wu et al., “Pilot’s visual attention allocation modeling under fatigue,” Technol. Heal. Care, vol. 23, no. s2, pp. S373–S381, 2015.
A. G. Richardson et al., “The effects of acute cortical somatosensory deafferentation on grip force control,” Cortex, vol. 74, pp. 1–8, 2016.
G. Y. Menshikova et al., “Eye Movements as Indicators of Vestibular Dysfunction,” Perception, vol. 0, no. 0, pp. 1–7, 2015.
C. Diels and J. E. Bos, “Self-driving carsickness,” Appl. Ergon., vol. 53, pp. 374–382, 2015.
C. Ho and C. Spence, “Affective multisensory driver interface design,” Int. J. Veh. Noise Vib., vol. 9, no. 1–2, pp. 61–74, 2013.
A. Lundkvist and A. Nykänen, “Response Times for Visual, Auditory and Vibrotactile Directional Cues in Driver Assistance Systems,” SAE Int. J. Transp. Saf., vol. 4, no. 1, Apr. 2016.
T. Hamaguchi et al., “Estimation of Driver Workload Based on the Model of Accelerator Pedal Control While Controlling Vehicle Speed,” SAE Tech. Pap., no. 2016–01–1412, Apr. 2016.
Y. Xiao et al., “Sustained Attention is Associated with Error Processing Impairment: Evidence from Mental Fatigue Study in Four-Choice Reaction Time Task.,” PLoS One, vol. 10, no. 3, p. e0117837, 2015.
A. Vakulin et al., “Behavioural Observation as a Means of Assessing Sleepiness Related Driving Impairment in Obstructive Sleep Apnoea,” Eat, Sleep, Work, vol. 1, 2015.
Y. Dong et al., “Driver inattention monitoring system for intelligent vehicles: A review,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 2, pp. 596–614, 2011.
K. Lienhard and S. S. Colson, “Relationship between lower limb muscle activity and platform acceleration during whole-body vibration exercise,” no. October, 2015.
N. Van der Stoep et al., “Multisensory interactions in the depth plane in front and rear space: A review,” Neuropsychologia, vol. 70, no. December, pp. 335–349, 2015.
S. Lee et al., “Effects of Active Noise Control on Physiological Functions,” J. Human-Environment Syst., vol. 12, no. 2, pp. 49–54, 2009.
H. M. Abd-Elfattah et al., “Physical and cognitive consequences of fatigue: A review,” J. Adv. Res., vol. 6, no. 3, pp. 351–358, 2015.
S. B. N. Thompson and S. Richer, “How Yawning and Cortisol Regulates the Attentional Network,” J. Neurosci. Rehabil., vol. 2, no. 1, pp. 1–9, 2015.
C. Ahlstrom et al., “Fit-for-duty test for estimation of drivers’ sleepiness level: Eye movements improve the sleep/wake predictor,” Transp. Res. Part C Emerg. Technol., vol. 26, pp. 20–32, 2013.
M. Gonzalez-Izal et al., “Electromyographic models to assess muscle fatigue,” J. Electromyogr. Kinesiol., vol. 22, no. 4, pp. 501–512, 2012.
D. Marlin, “The physiology of fatigue in the horse during exercise: What is fatigue? What causes it? How can you recognise and manage fatigue?” [Online]. Available: http://davidmarlin.co.uk/portfolio/the-physiology-of-fatigue-in-the-horse-during-exercise/. [Accessed: 17-Mar-2016].
J. Taylor et al., “Neural contributions to muscle fatigue: from the brain to the muscle and back again,” J. Med. Sci. Sport Exerc., 2016.
C. Froyd et al., “The development of peripheral fatigue and short-term recovery during self-paced high-intensity exercise,” J Physiol, vol. 591, no. 5, pp. 1339–1346, 2013.
D. Srinivasan et al., “Effects of concurrent physical and cognitive demands on muscle activity and heart rate variability in a repetitive upper-extremity precision task,” Eur. J. Appl. Physiol., vol. 116, no. 1, pp. 227–239, 2016.
C. Chen et al., “Visual fatigue caused by watching 3DTV : an fMRI study,” Biomed. Eng. Online, vol. 14, no. Suppl 1, p. S12, 2015.
J. F. Hopstaken et al., “A multifaceted investigation of the link between mental fatigue and task disengagement,” Psychophysiology, vol. 52, no. 3, pp. 305–315, 2015.
S. Zhao, “Study on Driver Model Parameters Distribution for Fatigue Driving Levels Based on Quantum Genetic Algorithm,” Open Cybern. Syst. J., vol. 9, pp. 1559–1566, 2015.
C. Solomon and Z. Wang, “Driver Attention and Behavior Detection with Kinect,” J. Image Graph., vol. 3, no. 2, pp. 84–89, 2015.
J. Paone et al., “Baseline face detection, head pose estimation, and coarse direction detection for facial data in the SHRP2 naturalistic driving study,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2015, vol. 2015-Augus, pp. 174–179.
C. K. Zope and Y. C. Kulkarni, “Driver Drowsiness Detection by Driver Aided System using Smartphone,” Int. J. IT, Eng. Appl. Sci. Res., vol. 4, no. 6, pp. 4–7, 2015.
A. Koenig et al., “Statistical sensor fusion of ECG data using automotive-grade sensors,” Adv. Radio Sci., vol. 13, pp. 197–202, 2015.
N. Munk et al., “Noninvasively measuring the hemodynamic effects of massage on skeletal muscle: A novel hybrid near-infrared diffuse optical instrument,” J. Bodyw. Mov. Ther., vol. 16, no. 1, pp. 22–28, 2012.
H. Qi et al., “Non-contact driver cardiac physiological monitoring using video data,” in IEEE China Summit and International Conference on nal and Information Processing, 2015, pp. 418–422.
R. Roy and K. Venkatasubramanian, “EKG/ECG based driver alert system for long haul drive,” Indian J. Sci. Technol., vol. 8, no. 19, pp. 8–13, 2015.
P. Konrad, The ABC of EMG: a practical introduction to kinesiological electromyography, no. 1. Scottsdale, AZ: Noraxon, 2005.
C. J. De Luca, “The Use of Surface Electromyography in Biomechanics,” J. Appl. Biomech., vol. 13, no. 2, pp. 135–163, 1997.
G. E. Loeb and C. Gans, Electromyography for Experimentalists. University of Chicago Press, 1986.
L. A. C. Kallenberg and H. J. Hermens, “Behaviour of a surface EMG based measure for motor control: Motor unit action potential rate in relation to force and muscle fatigue,” J. Electromyogr. Kinesiol., vol. 18, no. 5, pp. 780–788, 2008.
S. Thongpanja et al., “Mean and Median Frequency of EMG Signal to Determine Muscle Force Based on Time- Dependent Power Spectrum,” Elektron. IR Elektrotechnika, vol. 19, no. 3, pp. 51–56, 2013.
D. Moshou et al., “Dynamic muscle fatigue detection using self-organizing maps,” Appl. Soft Comput., vol. 5, no. 4, pp. 391–398, 2005.
A. F. T. Ibrahim et al., “Analysis of Electromyography (EMG) Signal for Human Arm Muscle: A Review,” in Advanced Computer and Communication Engineering Technology, Cham: Springer International Publishing, 2016, pp. 567–575.
M. Chappell and S. Payne, Physiology for Engineers, vol. 13. Cham: Springer International Publishing, 2016.
D. A. Dimitriev et al., “State Anxiety and Nonlinear Dynamics of Heart Rate Variability in Students,” PLoS One, vol. 11, no. 1, p. e0146131, 2016.
A. Davies and A. Scott, “ECG Basics,” in Starting to Read ECGs: The Basics, Cambridge, UK: Springer International Publishing, 2014, pp. 19–33.
A. D. Elliott and A. La Gerche, “The right ventricle following prolonged endurance exercise: are we overlooking the more important side of the heart? A meta-analysis.,” Br. J. Sports Med., no. June 2016, pp. 1–6, 2014.
E. Rogado et al., “Driver fatigue detection system,” 2008 IEEE Int. Conf. Robot. Biomimetics, ROBIO 2008, no. July 2015, pp. 1105–1110, 2008.
F. Chouchou et al., “Cardiac sympathetic modulation in response to apneas/hypopneas through heart rate variability analysis.,” PLoS One, vol. 9, no. 1, p. e86434, 2014.
L. M. B. H. De Rosario, J.S. Solaz, N. Rodr guez, “Controlled inducement and measurement of drowsiness in a driving simulator,” in IET Intelligent Transport system, 2010, pp. 280–288.
G. Matthews et al., “The Psychometrics of Mental Workload: Multiple Measures Are Sensitive but Divergent,” Hum. Factors J. Hum. Factors Ergon. Soc., vol. 57, no. 1, pp. 125–143, 2015.
Donald Scott, Understanding EEG, First Edit. Glasgow: Duckworth, 1976.
A. Dasgupta et al., “A vision-based system for monitoring the loss of attention in automotive drivers,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 4, pp. 1825–1838, 2013.
A. T. Bahill et al., “Dynamic overshoot in saccadic eye movements is caused by neurological control signed reversals.,” Exp. Neurol., vol. 48, no. 1, pp. 107–122, 1975.
Q. Ji et al., “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Trans. Veh. Technol., vol. 53, no. 4, pp. 1052–1068, 2004.
Y. Lin et al., “Eye Movement and Pupil Size Constriction Under Discomfort Glare,” Invest. Ophthalmol. Vis. Sci., vol. 56, no. 3, pp. 1649–1656, 2015.
K. Sanjaya et al., “The effects of different trunk inclinations on bilateral trunk muscular activities, centre of pressure and force exertions in static pushing postures,” J. Hum. Ergol. (Tokyo)., vol. 43, pp. 9–26, 2014.
K. H. Sanjaya et al., “The influence of laterality on different patterns of asymmetrical foot,” J. Hum. Ergol. (Tokyo)., vol. 43, pp. 77–92, 2014.
J. Townhill et al., “Using Actiwatch to monitor circadian rhythm disturbance in Huntington’ disease: A cautionary note,” J. Neurosci. Methods, pp. 1–6, 2016.
“ECG: Cardiology | Heart Sounds | Research | BIOPAC.” [Online]. Available: https://www.biopac.com/application/ecg-cardiology/advanced-feature/heart-sounds/. [Accessed: 24-May-2016].
M. G. Khan, “Step-by-Step Method for Accurate Electrocardiogram Interpretation,” in Contemporary Cardiology: Rapid ECG Interpretation, 3rd ed., 2008, pp. 25–80.
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