In long, stressful operational periods, military personnel face numerous challenges that may compromise their performance, an especially important one being fatigue. Current literature supports the view that behavioral, physiological, and cognitive factors are all predictive of the level of fatigue in individuals. However, much of the work on modeling fatigue has taken a narrow approach, relying only on a handful of modalities to measure fatigue. This paper aims to fill the void by providing an extensive overview of the current literature on both computationally measuring and modeling fatigue. We provide up-to-date and practical advice on which models are best suited for different situations and highlight directions for future work.
MillerNLMatsangasPKenneyA. The role of sleep in the military: implications for training and operational effectiveness. Oxford: Oxford University Press, 2011.
2.
GoodCHBragerAJCapaldiVF, et al. Sleep in the United States military. Neuropsychopharmacology2020; 45: 176–191.
3.
HarrisonYHorneJA. The impact of sleep deprivation on decision making: a review. J Exp Psychol Appl2000; 6: 236–249.
4.
KillgoreWDS. Effects of sleep deprivation on cognition. Progr Brain Res2010; 185: 105–129.
5.
DoranSMVan DongenHPDingesDF. Sustained attention performance during sleep deprivation: evidence of state instability. Arch Ital Biol2001; 139: 253–267.
6.
MillerNLMatsangasPShattuckL. Fatigue and its effect on performance in military environments. London: CRC Press, 2018.
7.
PhillipsRO. A review of definitions of fatigue—and a step towards a whole definition. Transp Res Part F Traff Psychol Behav2015; 29: 48–56.
8.
ShahidAWilkinsonKMarcuS, et al. STOP, THAT and one hundred other sleep scales. New York: Springer, 2012.
9.
Adão MartinsNRAnnaheimSSpenglerCM, et al. Fatigue monitoring through wearables: a state-of-the-art review. Front Physiol2021; 12: 790292.
10.
HurshSRRedmondDPJohnsonML, et al. Fatigue models for applied research in warfighting. Aviat Space Environ Med2004; 75: A44–A53; discussion A54.
ShiSYTangWZWangYY. A review on fatigue driving detection. ITM Web Conf2017; 12: 01019.
13.
PratamaBGArdiyantoIAdjiTB. A review on driver drowsiness based on image, bio-signal, and driver behavior. In: 2017 3rd international conference on science and technology—computer (ICST), Yogyakarta, Indonesia, 11–12 July 2017, pp. 70–75. New York: IEEE.
14.
ChowdhuryAShankaranRKavakliM, et al. Sensor applications and physiological features in drivers’ drowsiness detection: a review. IEEE Sens J2018; 18: 3055–3067.
15.
SikanderGAnwarS. Driver fatigue detection systems: a review. IEEE T Intell Transp Syst2019; 20: 2339–2352.
16.
XiaoYAbasAb. A review on fatigue driving detection. ASP T Internet Thing2021; 1: 1–14.
17.
HusseinMKSalmanTMMiryAH, et al. Driver drowsiness detection techniques: a survey. In: 2021 1st Babylon international conference on information technology and science (BICITS), Babil, Iraq, 28–29 April 2021, pp. 45–51. New York: IEEE.
18.
GawronVJ. Overview of self-reported measures of fatigue. Int J Aviat Psychol2016; 26: 120–131.
19.
MachadoMOKangNCTaiF, et al. Measuring fatigue: a meta-review. Int J Dermatol2021; 60: 1053–1069.
20.
NgxandeMTapamoJRBurkeM. Driver drowsiness detection using behavioral measures and machine learning techniques: a review of state-of-art techniques. In: 2017 pattern recognition association of South Africa and robotics and mechatronics (PRASA-RobMech), Bloemfontein, South Africa, 30 November–1 December, pp. 156–161. New York: IEEE.
21.
RamzanMKhanHUAwanSM, et al. A survey on state-of-the-art drowsiness detection techniques. IEEE Access2019; 7: 61904–61919.
22.
Chacon-MurguiaMIPrieto-ResendizC. Detecting driver drowsiness: a survey of system designs and technology. IEEE Cons Electr Mag2015; 4: 107–119.
23.
TianYWangK. A review of fatigue driving detection based on electrooculogram. In: 2021 4th international conference on computer science and software engineering (CSSE 2021), Singapore, 22–24 October 2021, pp. 99–102. New York: ACM.
24.
LiuFLiXLvT, et al. A review of driver fatigue detection: progress and prospect. In 2019 IEEE international conference on consumer electronics (ICCE), Las Vegas, NV, 11–13 January 2019, pp. 1–6. New York: IEEE.
25.
HuXLodewijksG. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: the value of differentiation of sleepiness and mental fatigue. J Safety Res2020; 72: 173–187.
26.
BalandongRPAhmadRFMohamad SaadMN, et al. A review on EEG-based automatic sleepiness detection systems for driver. IEEE Access2018; 6: 22908–22919.
27.
DeLucaJE. Fatigue as a window to the brain. Cambridge, MA: The MIT Press, 2005.
28.
AaronsonLSTeelCSCassmeyerV, et al. Defining and measuring fatigue. Image: The Journal of Nursing Scholarship1999; 31: 45–50.
29.
StulemeijerMFasottiLBleijenbergG. Fatigue after stroke. Fatigue as a Window to the Brain2005: 73–88.
30.
McMorrisTBarwoodMHaleBJ, et al. Cognitive fatigue effects on physical performance: a systematic review and meta-analysis. Physiol Behav2018; 188: 103–107.
31.
IshiiATanakaMWatanabeY. Neural mechanisms of mental fatigue. Reviews Neurosci2014; 25: 469–479.
32.
TranYCraigACraigR, et al. The influence of mental fatigue on brain activity: evidence from a systematic review with meta-analyses. Psychophysiology2020; 57: e13554.
33.
RustJGolombokS. Modern psychometrics: the science of psychological assessment. New York: Routledge, 2014.
34.
MillerNLShattuckLGMatsangasP. Sleep and fatigue issues in continuous operations: a survey of us army officers. Behavioral Sleep Medicine2011; 9: 53–65.
35.
HuangYLLiuRYWangQS, et al. Age-associated difference in circadian sleep–wake and rest–activity rhythms. Physiol Behav2002; 76: 597–603.
36.
LeBourgeoisMKGiannottiFCortesiF, et al. The relationship between reported sleep quality and sleep hygiene in Italian and American adolescents. Pediatrics2005; 115: 257–265.
37.
MendozaTRWangXSCleelandCS, et al. The rapid assessment of fatigue severity in cancer patients: use of the brief fatigue inventory. Cancer1999; 85: 1186–1196.
38.
ChalderTBerelowitzGPawlikowskaT, et al. Development of a fatigue scale. J Psycho Res1993; 37: 147–153.
39.
SpilsburyJCDrotarDRosenCL, et al. The Cleveland Adolescent Sleepiness Questionnaire: a new measure to assess excessive daytime sleepiness in adolescents. J Clin Sleep Med2007; 3: 603–612.
40.
SchwartzJEJandorfLKruppLB. The measurement of fatigue: a new instrument. J Psychosom Res1993; 37: 753–762.
41.
MichielsenHJDe VriesJVan HeckGL. Psychometric qualities of a brief self-rated fatigue measure: the Fatigue Assessment Scale. J Psychosom Res2003; 54: 345–352.
42.
HannDMDennistonMMBakerF. Measurement of fatigue in cancer patients: further validation of the Fatigue Symptom Inventory. Qual Life Res2000; 9: 847–854.
43.
LeeKAHicksGNino-MurciaG. Validity and reliability of a scale to assess fatigue. Psychiatry Res1991; 36: 291–298.
44.
FehnelSEBannCMHogueSL, et al. The development and psychometric evaluation of the Motivation and Energy Inventory (MEI). Qual Life Res2004; 13: 1321–1336.
45.
SmetsEMGarssenBBonkeB, et al. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res1995; 39: 315–325.
46.
BuysseDJReynoldsCF3rdMonkTH, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res1989; 28: 193–213.
47.
ViolaniCLucidiFRobustoE, et al. The assessment of daytime sleep propensity: a comparison between the Epworth Sleepiness Scale and a newly developed Resistance to Sleepiness Scale. Clin Neurophysiol2003; 114: 1027–1033.
48.
YiHShinKShinC. Development of the Sleep Quality Scale. J Sleep Res2006; 15: 309–316.
49.
MonkTHBuysseDJKennedyKS, et al. Measuring sleep habits without using a diary: the sleep timing questionnaire. Sleep2003; 26: 208–212.
50.
RosenthalLRoehrsTARothT. The sleep-wake activity inventory: a self-report measure of daytime sleepiness. Biol Psych1993; 34: 810–820.
51.
HoddesEZarconeVSmytheH, et al. Quantification of sleepiness: a new approach. Psychophysiology1973; 10: 431–436.
52.
BeurskensAJBültmannUKantI, et al. Fatigue among working people: validity of a questionnaire measure. Occup Environ Med2000; 57: 353–357.
53.
HartSGStavelandLE. Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv Psychol1988; 52: 139–183.
54.
AitkenRC. Measurement of feelings using visual analogue scales. Proc R Soc Med1969; 62: 989–993.
55.
LiuMZXuXHuJ, et al. Real time detection of driver fatigue based on CNN-LSTM. IET Image Process2022; 16: 576–595.
56.
PowellDMSpencerMBPetrieKJ. Fatigue in airline pilots after an additional day’s layover period. Aviat Space Environ Med2010; 81: 1013–1017.
57.
AkerstedtTGillbergM. Subjective and objective sleepiness in the active individual. Int J Neurosci1990; 52: 29–37.
58.
KaidaKTakahashiMAkerstedtT, et al. Validation of the Karolinska sleepiness scale against performance and EEG variables. Clin Neurophysiol2006; 117: 1574–1581.
59.
HasanMMWatlingCNLarueGS. Physiological signal-based drowsiness detection using machine learning: singular and hybrid signal approaches. J Safety Res2022; 80: 215–225.
60.
SamnSWPerelliLP. Estimating aircrew fatigue: a technique with application to airlift operations. Fort Belvoir, VA: Defense Technical Information Center, 1982.
61.
JohnsMW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep1991; 14: 540–545.
62.
MichielsenHJDe VriesJVan HeckGL. Psychometric qualities of a brief self-rated fatigue measure. J Psychosom Res2003; 54: 345–352.
ShattuckNLMatsangasP. Operational assessment of the 5-h on/10-h off watchstanding schedule on a US Navy ship: sleep patterns, mood and psychomotor vigilance performance of crewmembers in the nuclear reactor department. Ergonomics2016; 59: 657–664.
65.
RoachGDDawsonDLamondN. Can a shorter psychomotor vigilance task be used as a reasonable substitute for the ten-minute psychomotor vigilance task. Chronobiol Int2006; 23: 1379–1387.
66.
ChellappaYJoshiNNBharadwajV. Driver fatigue detection system. In: 2016 IEEE international conference on signal and image processing (ICSIP), Beijing, China, 13–15 August 2016, pp. 655–660. New York: IEEE.
67.
KnapikMCyganekB. Driver’s fatigue recognition based on yawn detection in thermal images. Neurocomputing2019; 338: 274–292.
68.
LiHWangYNanY. Motion fatigue state detection based on neural networks. Comput Intell Neurosci2022; 2022: 9602631.
69.
KassemHAChowdhuryMAbawajyJH. Drivers fatigue level prediction using facial, and head behavior information. IEEE Access2021; 9: 121686–121697.
70.
AnberSAlsaggafWShalashW. A hybrid driver fatigue and distraction detection model using AlexNet based on facial features. Electronics2022; 11: 285.
71.
LohaniMPayneBRStrayerDL. A review of psychophysiological measures to assess cognitive states in real-world driving. Front Hum Neurosci2019; 13: 57.
72.
WhitneyPHinsonJM. Measurement of cognition in studies of sleep deprivation. Prog Brain Res2010; 185: 37–48.
73.
TaylorDJMcFatterRM. Cognitive performance after sleep deprivation: does personality make a difference?Pers Indiv Diff2003; 34: 1179–1193.
74.
GoelNRaoHDurmerJS, et al. Neurocognitive consequences of sleep deprivation. Semin Neurol2009; 29: 320–339.
75.
TuckerAMWhitneyPBelenkyG, et al. Effects of sleep deprivation on dissociated components of executive functioning. Sleep2010; 33: 47–57.
76.
LimJDingesDF. Sleep deprivation and vigilant attention. Ann N Y Acad Sci2008; 1129: 305–322.
77.
Van DongenHMaislinGMullingtonJM, et al. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep2003; 26: 117–126.
78.
ChenXHouCLWangSB, et al. Decreased sustained attention, processing speed and verbal learning memory in patients with insomnia in Chinese young and middle-aged adults: a cross-sectional study. Sleep Biol Rhythm2020; 18: 225–232.
BinksPGWatersWFHurryM. Short-term total sleep deprivations does not selectively impair higher cortical functioning. Sleep1999; 22: 328–334.
81.
Pace-SchottEFHutchersonCABemporadB, et al. Failure to find executive function deficits following one night’s total sleep deprivation in university students under naturalistic conditions. Behav Sleep Med2009; 7: 136–163.
82.
KronholmESallinenMSuutamaT, et al. Self-reported sleep duration and cognitive functioning in the general population. J Sleep Res2009; 18: 436–446.
83.
SutterCZölligJAllemandM, et al. Sleep quality and cognitive function in healthy old age: the moderating role of subclinical depression. Neuropsychology2012; 26: 768–775.
84.
KillgoreWDBalkinTJWesenstenNJ. Impaired decision making following 49 h of sleep deprivation. J Sleep Res2006; 15: 7–13.
85.
LiuLZhouR. Effect of 72 h of sleep deprivation on the Iowa gambling task. Noro Psikiyatr Ars2016; 53: 357–360.
86.
CainSWSilvaEJChangAM, et al. One night of sleep deprivation affects reaction time, but not interference or facilitation in a Stroop task. Brain and Cognition2011; 76: 37–42.
87.
de BruinEJDewald-KaufmannJFOortFJ, et al. Differential effects of online insomnia treatment on executive functions in adolescents. Sleep Med2015; 16: 510–520.
88.
KillgoreWDKamimoriGHBalkinTJ. Caffeine improves the efficiency of planning and sequencing abilities during sleep deprivation. J Clin Psychopharmacol2014; 34: 660–662.
89.
MizunoKWatanabeY. Utility of an advanced trail making test as a neuropsychological tool for an objective evaluation of work efficiency during mental fatigue. In: YWatanabeBEvengårdBHNatelson, et al. (eds) Fatigue science for human health. Tokyo, Japan: Springer, 2008, pp. 47–54.
90.
DingesDFPowellJW. Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behav Res Meth Instr Comp1985; 17: 652–655.
91.
RuppTLWesenstenNJBliesePD, et al. Banking sleep: realization of benefits during subsequent sleep restriction and recovery. Sleep2009; 32: 311–321.
92.
MiyakeAFriedmanNPEmersonMJ, et al. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn Psychol2000; 41: 49–100.
93.
ThorndikeELBregmanEOCobbMV, et al. & Inst of Educational Research Div of Psychology. Teachers Coll, Columbia U. The measurement of intelligence. Teachers College Bureau of Publications; 1926. https://doi.org/10.1037/11240-000
94.
BecharaADamasioARDamasioH, et al. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition1994; 50: 7–15.
95.
SternbergS. High-speed scanning in human memory. Science1966; 153: 652–654.
96.
GordonJKYoungMGarciaC. Why do older adults have difficulty with semantic fluency. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn2018; 25: 803–828.
97.
TombaughTNKozakJReesL. Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Arch Clin Neuropsychol1999; 14: 167–177.
98.
LimJDingesDF. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychol Bull2010; 136: 375–389.
99.
GunzelmannGGrossJBGluckKA, et al. Sleep deprivation and sustained attention performance: integrating mathematical and cognitive modeling. Cogn Sci2009; 33: 880–910.
100.
SunYLimJMengJ, et al. Discriminative analysis of brain functional connectivity patterns for mental fatigue classification. Ann Biomed Eng2014; 42: 2084–2094.
101.
DongBTLinHY. An on-board monitoring system for driving fatigue and distraction detection. In: 2021 22nd IEEE international conference on industrial technology (ICIT), Valencia, 10–12 March 2021, vol. 1, pp. 850–855. New York: IEEE.
102.
PandaSKolhekarM. Feature selection for driver drowsiness detection. In: NChakiNDevarakondaASarkar, et al. (eds) Proceedings of international conference on computational intelligence and data engineering. Singapore: Springer, 2019, pp. 127–140.
103.
UlinskasMWoźniakMDamaševičiusR. Analysis of keystroke dynamics for fatigue recognition. In: International conference on computational science and its applications, Trieste, 3–6 July 2017, pp. 235–247. Cham: Springer.
104.
LiuFLiXLvT, et al. A review of driver fatigue detection: progress and prospect. In: 2019 IEEE international conference on consumer electronics (ICCE), Las Vegas, NV, 11–13 January 2019, pp. 1–6. New York: IEEE.
105.
BamideleAAKamardinKAbd AzizNSN, et al. Non-intrusive driver drowsiness detection based on face and eye tracking. Int J Adv Comp Sci Appl2019; 10: 549–568.
106.
XiaoYbin AbasA. A review on fatigue driving detection. Guangzhou, Guangdong: Advancing Science Press Limited, pp. 1–14.
107.
WangQYangJRenM, et al. Driver fatigue detection: a survey. In: 2006 6th world congress on intelligent control and automation, Dalian, China, 21–23 June 2006, vol. 2, pp. 8587–8591. New York: IEEE.
NakamuraTMaejimaAMorishimaS. Driver drowsiness estimation from facial expression features computer vision feature investigation using a CG model. In: 2014 international conference on computer vision theory and applications (VISAPP), Lisbon, 5–8 January 2014, vol. 2, pp. 207–214. New York: IEEE.
110.
BafnaTHansenJP. Mental fatigue measurement using eye metrics: a systematic literature review. Psychophysiology2021; 58: e13828.
111.
SchweizerTWyssTGilgen-AmmannR. Detecting soldiers’ fatigue using eye-tracking glasses: practical field applications and research opportunities. Milit Med2021; 187: 404–409.
112.
LiJLiHUmerW, et al. Identification and classification of construction equipment operators’ mental fatigue using wearable eye-tracking technology. Auto Construct2020; 109: 103000.
113.
ShenZWeiY. A high-precision feature extraction network of fatigue speech from air traffic controller radiotelephony based on improved deep learning. ICT Expr2021; 7: 403–413.
114.
QinHZhouXOuX, et al. Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight. Hum Factor Ergon Man2021; 31: 637–651.
115.
SampeiKOgawaMTorresCCC, et al. Mental fatigue monitoring using a wearable transparent eye detection system. Micromachines2016; 7: 20.
116.
RussellBMcDaidAToscanoW, et al. Predicting fatigue in long duration mountain events with a single sensor and deep learning model. Sensors2021; 21: 5442.
117.
YangGLinYBhattacharyaP. A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inform Sci2010; 180: 1942–1954.
118.
StancinICifrekMJovicA. A review of EEG signal features and their application in driver drowsiness detection systems. Sensors2021; 21: 3786.
119.
FanCPengYPengS, et al. Detection of train driver fatigue and distraction based on forehead EEG: a time-series ensemble learning method. IEEE T Intell Transp Syst2022; 23: 13559–13569.
120.
MonteiroTGSkourupCZhangH. Using EEG for mental fatigue assessment: a comprehensive look into the current state of the art. IEEE T Hum-Mach Syst2019; 49: 599–610.
121.
YuQKeXYangD, et al. Mental fatigue testing based on deep learning. In: 2020 IEEE 9th joint international information technology and artificial intelligence conference (ITAIC), Chongqing, China, 11–13 December 2020, vol. 9, pp. 32–35. New York: IEEE.
122.
SanPPLingSHChaiR, et al. EEG-based driver fatigue detection using hybrid deep generic model. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Orlando, FL, 16–20 August 2016, pp. 800–803. New York: IEEE.
123.
BaiYGuanYNgWF. Fatigue assessment using ECG and actigraphy sensors. In: Proceedings of the 2020 international symposium on wearable computers, Virtual Event Mexico, 12–16 September 2020, pp. 12–16. New York: ACM.
124.
LeeHLeeJShinM. Using wearable ECG/PPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots. Electronics2019; 8: 192.
125.
JiangYGuoSDengS. Denoising and chaotic feature extraction of electrocardial signals for driver fatigue detection by Kolmogorov entropy. J Dyn Syst Meas Contr2019; 141: 021013.
126.
LeeBGLeeBLChungWY. Smartwatch-based driver alertness monitoring with wearable motion and physiological sensor. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Milan, 25–29 August 2015, pp. 6126–6129. New York: IEEE.
127.
CheonSPKangSJ. Sensor-based driver condition recognition using support vector machine for the detection of driver drowsiness. In: 2017 IEEE intelligent vehicles symposium (IV), Los Angeles, CA, 11–14 June 2017, pp. 1517–1522. New York: IEEE.
128.
KundingerTYalavarthiPKRienerA, et al. Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups. Int J Perv Comput Commun2020; 16: 1–23.
129.
YueYLiuDFuS, et al. Heart rate and heart rate variability as classification features for mental fatigue using short-term ppg signals via smartphones instead of ECG recordings. In: 2021 13th international conference on communication software and networks (ICCSN), Chongqing, China, 4–7 June 2021, pp. 370–376. New York: IEEE.
130.
LemkaddemADelgado-GonzaloRTüretkenE, et al. Multi-modal driver drowsiness detection: a feasibility study. In: 2018 IEEE EMBS international conference on biomedical & health informatics (BHI), Las Vegas, NV, 4–7 March 2018, pp. 9–12. New York: IEEE.
131.
Jacobé de NauroisCBourdinCBougardC, et al. Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness. Accid Anal Prev2018; 121: 118–128.
132.
LiGChungWY. Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier. Sensors2013; 13: 16494–16511.
133.
KhanMJHongKSNaseerN, et al. Drowsiness detection in dorsolateral-prefrontal cortex using fNIRS for a passive-BCI. In: 2015 15th international conference on control, automation and systems (ICCAS), Busan, South Korea, 13–16 October 2015, pp. 1811–1816. New York: IEEE.
134.
KhanMJLiuXBhuttaMR, et al. Drowsiness detection using fNIRS in different time windows for a passive BCI. In: 2016 6th IEEE international conference on biomedical robotics and biomechatronics (BioRob), Singapore, 26–29 June 2016, pp. 227–231. New York: IEEE.
135.
AhnSNguyenTJangH, et al. Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and FNIRS data. Front Hum Neurosci2016; 10: 219.
136.
SoneTYagiT. Drowsiness detection by skin potential activity. In: The 6th 2013 biomedical engineering international conference, Amphur Muang, Thailand, 23–25 October 2013, pp. 1–5. New York: IEEE.
137.
Boon-LengLDae-SeokLBoon-GiinL. Mobile-based wearable-type of driver fatigue detection by GSR and EMG. In: TENCON 2015-2015 IEEE region 10 conference, Macao, China, 1–4 November 2015, pp. 1–4. New York: IEEE.
138.
GennoHIshikawaKKanbaraO, et al. Using facial skin temperature to objectively evaluate sensations. Int J Indus Erg1997; 19: 161–171.
139.
BaiYGuanYShiJQ, et al. Towards automated fatigue assessment using wearable sensing and mixed-effects models. In: 2021 international symposium on wearable computers, Virtual, 21–26 September 2021, pp. 129–131. New York: ACM.
140.
GielenJAertsJM. Feature extraction and evaluation for driver drowsiness detection based on thermoregulation. Appl Sci2019; 9: 3555.
141.
KoraganjiVNWhelanAJMokkapatiA, et al. Performance of 1D-CNNS for EEG-based mental state classification: effects of domain, window size and electrode montage. In: 2021 IEEE international conference on systems, man, and cybernetics (SMC), Melbourne, Australia, 17–20 October 2021, pp. 2664–2671. New York: IEEE.
142.
HuJ. Comparison of different features and classifiers for driver fatigue detection based on a single EEG channel. Comput Math Methods Med2017; 2017: 5109530.
143.
MuZHuJMinJ. Driver fatigue detection system using electroencephalography signals based on combined entropy features. Appl Sci2017; 7: 150.
144.
ZhangXLuDPanJ, et al. Fatigue detection with covariance manifolds of electroencephalography in transportation industry. IEEE T Indus Inf2020; 17: 3497–3507.
145.
SitaramRCariaABirbaumerN. Hemodynamic brain–computer interfaces for communication and rehabilitation. Neural Netw2009; 22: 1320–1328.
146.
ShinJVon LühmannAKimDW, et al. Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset. Sci Data2018; 5: 1–16.
147.
van Ravenswaaij-ArtsCMKolleeLAHopmanJC, et al. Heart rate variability. Ann Int Med1993; 118: 436–447.
148.
LinYYHsiungPA. An early warning system for predicting driver fatigue. In: 2017 IEEE international conference on consumer electronics-Taiwan (ICCE-TW), Taipei, 12–14 June 2017, pp. 283–284. New York: IEEE.
149.
UtomoDYangTHThanhDT, et al. Driver fatigue prediction using different sensor data with deep learning. In: 2019 IEEE international conference on industrial cyber physical systems (ICPS), Taipei, 6–9 May 2019, pp. 242–247. New York: IEEE.
150.
AljihmaniLUrsutiuDCornelS, et al. Machine learning for fatigue estimation and prediction “an introduction study.” In: SVladNMRoman (eds) International conference on advancements of medicine and health care through technology. Cham: Springer, 2022, pp. 226–231.
151.
ZhaoYXieKZouZ, et al. Intelligent recognition of fatigue and sleepiness based on inceptionV3-LSTM via multi-feature fusion. IEEE Access2020; 8: 144205–144217.
152.
CherianVABhardwajRBalasubramanianV. Real-time driver fatigue detection from ECG using deep learning algorithm. In: MMuzammilAAKhanFHasan (eds) Ergonomics for improved productivity. Singapore: Springer, 2021, pp. 615–621.
153.
LinWHWuDLiC, et al. Comparison of heart rate variability from PPG with that from ECG. In: YTZhang (ed.) The international conference on health informatics. Cham: Springer, 2014, pp. 213–215.
154.
PinheiroNCouceiroRHenriquesJ, et al. Can PPG be used for HRV analysis? In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Orlando, FL, USA, 16–20 August 2016, pp. 2945–2949. New York: IEEE.
155.
SharmaMKackerSSharmaM. A brief introduction and review on galvanic skin response. Int J Med Res Prof2016; 2: 13–17.
156.
Boon-LengLDae-SeokLBoon-GiinL. Mobile-based wearable-type of driver fatigue detection by GSR and EMG. In: TENCON 2015—2015 IEEE region 10 conference, Macao, China, 1–4 November 2015, pp. 1–4. New York: IEEE.
157.
ChoiMKooGSeoM, et al. Wearable device-based system to monitor a driver’s stress, fatigue, and drowsiness. IEEE T Instrum Meas2017; 67: 634–645.
158.
Díaz-GarcíaJGonzález-PonceIPonce-BordónJC, et al. Mental load and fatigue assessment instruments: a systematic review. Int J Env Res Pub He2021; 19: 419.
159.
BendakSRashidHS. Fatigue in aviation: a systematic review of the literature. Int J Ind Ergonom2020; 76: 102928.
160.
SmithiesTDTothAJDunicanIC, et al. The effect of sleep restriction on cognitive performance in elite cognitive performers: a systematic review. Sleep2021; 44: zsab008.
161.
HornsbyBWNaylorGBessFH. A taxonomy of fatigue concepts and their relation to hearing loss. Ear Hear2016; 37: 136S–144S.
162.
Kodithuwakku ArachchigeSNKBurchVRFChanderH, et al. The use of wearable devices in cognitive fatigue: current trends and future intentions. Theor Iss Erg Sci2022; 23: 374–386.
163.
RussellSJ. Artificial intelligence a modern approach. London: Pearson Education, 2010.
164.
AhlstromCvan LeeuwenWKrupeniaS, et al. Real-time adaptation of driving time and rest periods in automated long-haul trucking: development of a system based on biomathematical modelling, fatigue and relaxation monitoring. IEEE Trans Intell Transp Syst2022; 23: 4758–4766.
165.
RamakrishnanSWesenstenNJKamimoriGH, et al. A unified model of performance for predicting the effects of sleep and caffeine. Sleep2016; 39: 1827–1841.
166.
JasonLJessenTPorterN, et al. Examining types of fatigue among individuals with ME/CFS. Disab Stud Quarter2009; 29: 9.
167.
LiuJRamakrishnanSLaxminarayanS, et al. Real-time individualization of the unified model of performance. J Sleep Res2017; 26: 820–831.
168.
LiFChenCHZhengP, et al. An explorative context-aware machine learning approach to reducing human fatigue risk of traffic control operators. Safety Sci2020; 125: 104655.
169.
GoumopoulosCPothaN. Mental fatigue detection using a wearable commodity device and machine learning. J Amb Intell Hum Comp. Epub ahead of print 28January2022. DOI: 10.1007/s12652-021-03674-z.
170.
BoolaniARyanJVoT, et al. Do changes in mental energy and fatigue impact functional assessments associated with fall risks? An exploratory study using machine learning. Phys Occup Ther Geriatr2020; 38: 283–301.
171.
DawsonDDarwentDRoachGD. How should a bio-mathematical model be used within a fatigue risk management system to determine whether or not a working time arrangement is safe. Accid Anal Prev2017; 99: 469–473.
172.
WolfsonARCarskadonMA. Sleep schedules and daytime functioning in adolescents. Child Dev1998; 69: 875–887.
173.
VitaternaMHTakahashiJSTurekFW. Overview of circadian rhythms. Alcohol Res Heal2001; 25: 85.
174.
BorbélyAA. A two process model of sleep regulation. Hum Neurobiol1982; 1: 195–204.
175.
GunzelmannGGluckK. An integrative approach to understanding and predicting the consequences of fatigue on cognitive performance. Technical Report, Human Effectiveness Directorate, Air Force Research Lab, Mesa City, AZ, 2009.
176.
AkerstedtTFolkardS. A model of human sleepiness. Sleep1990; 90: 310–313.
177.
AkerstedtTConnorJGrayA, et al. Predicting road crashes from a mathematical model of alertness regulation—the sleep/wake predictor. Accid Anal Prev2008; 40: 1480–1485.
178.
WilsonMDBallardTStricklandL, et al. Understanding fatigue in a naval submarine: applying biomathematical models and workload measurement in an intensive longitudinal design. Appl Ergon2021; 94: 103412.
179.
RajdevPThorsleyDRajaramanS, et al. A unified mathematical model to quantify performance impairment for both chronic sleep restriction and total sleep deprivation. J Theor Biol2013; 331: 66–77.
180.
ChuiCChenG. Kalman filtering with real time applications. Appl Optics1989; 28: 1841.
181.
PengHTBouakFWangW, et al. An improved model to predict performance under mental fatigue. Ergonomics2018; 61: 988–1003.
182.
Flynn-EvansEEKirkleyCYoungM, et al. Changes in performance and bio-mathematical model performance predictions during 45 days of sleep restriction in a simulated space mission. Sci Rep2020; 10: 1–14.
183.
RoachGDFletcherADawsonD. A model to predict work-related fatigue based on hours of work. Aviat Space Environ Med2004; 75: A61–A69; discussion A70.
184.
RiedySMFekedulegnDAndrewM, et al. Generalizability of a biomathematical model of fatigue’s sleep predictions. Chronobiol Int2020; 37: 564–572.
185.
McCauleyPKalachevLVSmithAD, et al. A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance. J Theor Biol2009; 256: 227–239.
186.
McCauleyPKalachevLVMolliconeDJ, et al. Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance. Sleep2013; 36: 1987–1997.
187.
WilsonMDStricklandLBallardT. FIPS: an R package for biomathematical modelling of human fatigue related impairment. J Open Source Softw2020; 5: 2340.
188.
MitchellTMMitchellTM. Machine learning, vol. 1. New York: McGraw-Hill, 1997.
189.
PedregosaFVaroquauxGGramfortA, et al. Scikit-learn: machine learning in Python. J Mach Learn Res2011; 12: 2825–2830.
190.
ChuYWuQ. Recognition of fatigue status of pilots based on deep contractive sparse auto-encoding network. In: 2018 37th Chinese control conference (CCC), Wuhan, China, 25–27 July 2018, pp. 9220–9225. New York: IEEE.
191.
WuEQZhouMHuD, et al. Self-paced dynamic infinite mixture model for fatigue evaluation of pilots’ brains. IEEE Trans Cybern2022; 52: 5623–5638.
192.
MonteiroTGLiGSkourupC, et al. Investigating an integrated sensor fusion system for mental fatigue assessment for demanding maritime operations. Sensors2020; 20: 2588.
193.
MonteiroTGSkourupCZhangH. A task agnostic mental fatigue assessment approach based on EEG frequency bands for demanding maritime operation. IEEE Instr Meas Mag2021; 24: 82–88.
194.
KoLWLaiWKLiangWG, et al. Single channel wireless EEG device for real-time fatigue level detection. In: 2015 international joint conference on neural networks (IJCNN), Killarney, 12–17 July 2015, pp. 1–5. New York: IEEE.
195.
KoLWKomarovOLaiWK, et al. Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task. J Neural Eng2020; 17: 036015.
196.
SubhaRAravindJSanthalingamV, et al. Drowsy driving detection system by analyzing and classifying brain waves. In: 2018 second international conference on electronics, communication and aerospace technology (ICECA), Coimbatore, India, 29–31 March 2018, pp. 667–672. New York: IEEE.
197.
ZhaoLWangZWangX, et al. Driver drowsiness detection using facial dynamic fusion information and a DBN. IET Intell Transp Syst2018; 12: 127–133.
198.
AbbasQ. HybridFatigue: a real-time driver drowsiness detection using hybrid features and transfer learning. Int J Adv Comp Sci Appl2020; 11: 585–593.
199.
KundingerTRienerA. The potential of wrist-worn wearables for driver drowsiness detection: a feasibility analysis. In: Proceedings of the 28th ACM conference on user modeling, adaptation and personalization, Genoa, 14–17 July 2020, pp. 117–125. New York: ACM.
200.
ShahrudinNSNSidekK. Driver drowsiness detection using different classification algorithms. J Phys Conf Ser2020; 1502: 012037.
201.
KartschVBenattiSGuermandiM, et al. Ultra low-power drowsiness detection system with biowolf. In: 2019 9th international IEEE/EMBS conference on neural engineering (NER), San Francisco, CA, USA, 20–23 March 2019, pp. 1187–1190. New York: IEEE.
202.
HuangSLiJZhangP, et al. Detection of mental fatigue state with wearable ECG devices. Int J Med Inform2018; 119: 39–46.
203.
HanWYangYHuangGB, et al. Driver drowsiness detection based on novel eye openness recognition method and unsupervised feature learning. In: 2015 IEEE international conference on systems, man, and cybernetics, Hong Kong, China, 9–12 October 2015, pp. 1470–1475. New York: IEEE.
204.
YinZZhangJ. Task-generic mental fatigue recognition based on neurophysiological signals and dynamical deep extreme learning machine. Neurocomputing2018; 283: 266–281.
205.
ChenJWangHHuaC. Electroencephalography based fatigue detection using a novel feature fusion and extreme learning machine. Cogn Syst Res2018; 52: 715–728.
206.
ChenLZhiXWangH, et al. Driver fatigue detection via differential evolution extreme learning machine technique. Electronics2020; 9: 1850.
207.
ChenJWangHHuaC. Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks. Int J Psychophysiol2018; 133: 120–130.
208.
HuJMinJ. Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model. Cogn Neurodyn2018; 12: 431–440.
XuYYangZLiG, et al. A practical application for quantitative brain fatigue evaluation based on machine learning and ballistocardiogram. Healthcare2021; 9: 1453. https://www.mdpi.com/2227-9032/9/11/1453
211.
HachisukaS. Human and vehicle-driver drowsiness detection by facial expression. In: 2013 international conference on biometrics and Kansei engineering, Tokyo, Japan, 5–7 July 2013, pp. 320–326. New York: IEEE.
212.
AmirudinNABSaadNAliSSA, et al. Detection and analysis of driver drowsiness. In: 2018 3rd international conference on emerging trends in engineering, sciences and technology (ICEEST), Karachi, Pakistan, 21–22 December 2018, pp. 1–9. New York: IEEE.
213.
QianKKoikeTNakamuraT, et al. Learning multimodal representations for drowsiness detection. IEEE T Intell Transp Syst2022; 23: 11539–11548.
214.
MahmoodiMNahviA. Driver drowsiness detection based on classification of surface electromyography features in a driving simulator. Proc Inst Mech Eng H2019; 233: 395–406.
215.
BalamVPChinaraS. Statistical channel selection method for detecting drowsiness through single-channel EEG-based BCI system. IEEE Trans Instr Meas2021; 70: 1–9.
216.
WangHXuLBezerianosA, et al. Linking attention-based multiscale CNN with dynamical GCN for driving fatigue detection. IEEE Trans Instr Meas2020; 70: 1–11.
217.
LinZQiuTLiuP, et al. Fatigue driving recognition based on deep learning and graph neural network. Biomed Sig Process Contr2021; 68: 102598.
218.
WangZZhaoYHeY, et al. Phase lag index-based graph attention networks for detecting driving fatigue. Rev Sci Instr2021; 92: 094105.
219.
DazaIGHernándezNBergasaLM, et al. Drowsiness monitoring based on driver and driving data fusion. In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC), Washington, DC, 5–7 October 2011, pp. 1199–1204. New York: IEEE.
220.
MatsuoHKhiatA. Prediction of drowsy driving by monitoring driver’s behavior. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), Tsukuba, Japan, 11–15 November 2012, pp. 3390–3393. New York: IEEE.
221.
HajinorooziMZhangJMHuangY. Driver’s fatigue prediction by deep covariance learning from EEG. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC), Banff, AB, Canada, 5–8 October 2017, pp. 240–245. New York: IEEE.
222.
ZouBShenMLiX, et al. EEG-based driving fatigue detection during operating the steering wheel data section. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), Montreal, QC, Canada, 20–24 July 2020, pp. 248–251. New York: IEEE.
223.
ZhaoLWangZWangX, et al. Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning. J Electr Imag2016; 25: 053024.
224.
WuEQPengXZhangCZ, et al. Pilots’ fatigue status recognition using deep contractive autoencoder network. IEEE T Instrum Meas2019; 68: 3907–3919.
225.
wuEQDengPYQiuXY, et al. Detecting fatigue status of pilots based on deep learning network using EEG signals. IEEE T Cognit Develop Syst2021; 13: 575–585.
226.
TadesseEShengWLiuM. Driver drowsiness detection through HMM based dynamic modeling. In: 2014 IEEE international conference on robotics and automation (ICRA), Hong Kong, China, 31 May–7 June 2014, pp. 4003–4008. New York: IEEE.
227.
FuRWangHZhaoW. Dynamic driver fatigue detection using hidden Markov model in real driving condition. Exp Syst Appl2016; 63: 397–411.
228.
ChoiIHJeongCHKimYG. Tracking a driver’s face against extreme head poses and inference of drowsiness using a hidden Markov model. Appl Sci2016; 6: 137.
229.
WuEQZhuLMLiGJ, et al. Nonparametric hierarchical hidden semi-Markov model for brain fatigue behavior detection of pilots during flight. IEEE Trans Intell Transp Syst2021; 23: 5245–5256.
230.
SchwartzDMitchellT. Understanding language-elicited EEG data by predicting it from a fine-tuned language model. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) 2019 June, pp. 43–57.
231.
DwivediKBiswaranjanKSethiA. Drowsy driver detection using representation learning. In: 2014 IEEE international advance computing conference (IACC), Gurgaon, India, 21–22 February 2014, pp. 995–999. New York: IEEE.
232.
HuynhXPParkSMKimYG. Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine. In: CSChenJLuKKMa (eds) Asian conference on computer vision. Cham: Springer, 2017, pp. 134–145.
233.
ReddyBKimYHYunS, et al. Real-time driver drowsiness detection for embedded system using model compression of deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Honolulu, HI, 21–26 July 2017, pp. 121–128. New York: IEEE.
234.
ChirraVRRUyyalaSRKolliVKK. Deep CNN: a machine learning approach for driver drowsiness detection based on eye state. Rev D’intell Artif2019; 33: 461–466.
235.
JabbarRShinoyMKharbecheM, et al. Driver drowsiness detection model using convolutional neural networks techniques for android application. In: 2020 IEEE international conference on informatics, IoT, and enabling technologies (ICIoT), Doha, Qatar, 2–5 February 2020, pp. 237–242. New York: IEEE.
236.
ChaabeneSBouazizBBoudayaA, et al. Convolutional neural network for drowsiness detection using EEG signals. Sensors2021; 21: 1734.
237.
ZhouCLiJ. A real-time driver fatigue monitoring system based on lightweight convolutional neural network. In: 2021 33rd Chinese control and decision conference (CCDC), Kunming, China, 22–24 May 2021, pp. 1548–1553. New York: IEEE.
238.
YanYDingSYueZ, et al. A non-contact mental fatigue detection method for space medical experiment using multi-feature fusion model. In: 2021 IEEE international conferences on internet of things (iThings) and IEEE green computing & communications (GreenCom) and IEEE cyber, physical & social computing (CPSCom) and IEEE smart data (SmartData) and IEEE congress on cybermatics (Cybermatics), Melbourne, Australia, 6–8 December 2021, pp. 197–203. New York: IEEE.
239.
Ul AlamMA. Activity-aware deep cognitive fatigue assessment using wearables. Annu Int Conf IEEE Eng Med Biol Soc2021; 2021: 7433–7436.
240.
ZhangXLuDShenJ, et al. Spatial-temporal joint optimization network on covariance manifolds of electroencephalography for fatigue detection. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM), Seoul, South Korea, 16–19 December 2020, pp. 893–900. New York: IEEE.
241.
BudakUBajajVAkbulutY, et al. An effective hybrid model for EEG-based drowsiness detection. IEEE Sens J2019; 19: 7624–7631.
242.
JiaoYDengYLuoY, et al. Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks. Neurocomputing2020; 408: 100–111.
243.
AnsariSNaghdyFDuH, et al. Driver mental fatigue detection based on head posture using new modified RELU-BILSTM deep neural network. IEEE T Intell Transp Syst2022; 23: 10957–10969.
244.
ZengHLiXBorghiniG, et al. An EEG-based transfer learning method for cross-subject fatigue mental state prediction. Sensors (Basel, Switzerland)2021; 21: 2369.
245.
ZhengRWangZHeY, et al. EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition. Cogn Neurodyn2022; 16: 325–336.
246.
StancinIFridNCifrekM, et al. EEG signal multichannel frequency-domain ratio indices for drowsiness detection based on multicriteria optimization. Sensors2021; 21: 6932.
247.
De La VegaRAnabalónHJaraC, et al. Effectiveness of mobile technology in managing fatigue: Balert app. Front Psychol2021; 12: 704955.
248.
LiuYLanZCuiJ, et al. EEG-based cross-subject mental fatigue recognition. In: 2019 international conference on cyberworlds (CW), Kyoto, Japan, 2–4 October 2019, pp. 247–252. New York: IEEE.
249.
SabetMZoroofiRASadeghniiat-HaghighiK, et al. A new system for driver drowsiness and distraction detection. In: 20th Iranian conference on electrical engineering (ICEE2012), Tehran, Iran, 15–17 May 2012, pp. 1247–1251. New York: IEEE.
250.
PunithaAGeethaMKSivaprakashA. Driver fatigue monitoring system based on eye state analysis. In: 2014 international conference on circuits, power and computing technologies [ICCPCT-2014], Nagercoil, India, 20–21 March 2014, pp. 1405–1408. New York: IEEE.
251.
PaulyLSankarD. Detection of drowsiness based on HOG features and SVM classifiers. In: 2015 IEEE international conference on research in computational intelligence and communication networks (ICRCICN), Kolkata, India, 20–22 November 2015, pp. 181–186. New York: IEEE.
252.
ManuB. Facial features monitoring for real time drowsiness detection. In: 2016 12th international conference on innovations in information technology (IIT), Al Ain, United Arab Emirates, 28–30 November 2016, pp. 1–4. New York: IEEE.
253.
OginoMMitsukuraY. Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram. Sensors2018; 18: 4477.
254.
MehreenAAnwarSMHaseebM, et al. A hybrid scheme for drowsiness detection using wearable sensors. IEEE Sens J2019; 19: 5119–5126.
255.
DeySChowdhurySASultanaS, et al. Real time driver fatigue detection based on facial behaviour along with machine learning approaches. In: 2019 IEEE international conference on signal processing, information, communication & systems (SPICSCON), Dhaka, Bangladesh, 28–30 November 2019, pp. 135–140. New York: IEEE.
256.
JiangYMalliarasPChenB, et al. Real-time forecasting of exercise-induced fatigue from wearable sensors. Comput Biol Med2022; 148: 105905.
257.
TamhaneACDunlopDD. Statistics and data analysis: from elementary to intermediate. London: Pearson, 2000.
258.
ChomboonKChujaiPTeerarassameeP, et al. An empirical study of distance metrics for k-nearest neighbor algorithm. In: Proceedings of the 3rd international conference on industrial application engineering, Nanjing, China, 27–28 May 2022, pp. 280–285. New York: ACM.
LeCunYBoserBDenkerJS, et al. Backpropagation applied to handwritten zip code recognition. Neur Comput1989; 1: 541–551.
268.
BalamVPSameerVUChinaraS. Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram. IET Intell Transp Syst2021; 15: 514–524.
269.
JiaHXiaoZJiP. Fatigue driving detection based on deep learning and multi-index fusion. IEEE Access2021; 9: 147054–147062.
270.
HochreiterSSchmidhuberJ. Long short-term memory. Neur Comput1997; 9: 1735–1780.
271.
Venkata PhanikrishnaBJaya PrakashASuchismithaC. Deep review of machine learning techniques on detection of drowsiness using EEG signal. IETE J Res. Epub ahead of print 5May2021. DOI: 10.1080/03772063.2021.1913070.
272.
ScarselliFGoriMTsoiAC, et al. The graph neural network model. IEEE Trans Neur Netw2008; 20: 61–80.
273.
HuangGBZhuQYSiewCK. Extreme learning machine: theory and applications. Neurocomputing2006; 70: 489–501.
274.
JamshidiSAzmiRSharghiM, et al. Hierarchical deep neural networks to detect driver drowsiness. Multimed Tools Appl2021; 80: 16045–16058.
275.
ZhangYChenYPanZ. A deep temporal model for mental fatigue detection. In: 2018 IEEE international conference on systems, man, and cybernetics (SMC), Miyazaki, Japan, 7–10 October 2018, pp. 1879–1884. New York: IEEE.
276.
ChenLXinGLiuY, et al. Driver fatigue detection based on facial key points and LSTM. Secur Commun Netw2021; 2021: 5383573.
277.
MittalAKumarKDhamijaS, et al. Head movement-based driver drowsiness detection: a review of state-of-art techniques. In: 2016 IEEE international conference on engineering and technology (ICETECH), Coimbatore, India, 17–18 March 2016, pp. 903–908. New York: IEEE.
278.
GonzalezKSasangoharFMehtaRK, et al. Measuring fatigue through heart rate variability and activity recognition: a scoping literature review of machine learning techniques. Proc Hum Fac Ergonom Soc Ann Meet2017; 61: 1748–1752.
279.
KniplingRRauP. PERCLOS: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance. Washington, DC: Federal Highway Administration (FHWA), 1998.
280.
SoukupovaTCechJ. Eye blink detection using facial landmarks. In: 21st computer vision winter workshop, Rimske Toplice, 3–5 February 2016.
281.
NakamuraTMaejimaAMorishimaS. Detection of driver’s drowsy facial expression. In: 2013 2nd IAPR Asian conference on pattern recognition, Naha, Japan, 5–8 November 2013, pp. 749–753. New York: IEEE.
282.
AL-AnizyGJNordinMJRazooqMM. Automatic driver drowsiness detection using Haar algorithm and support vector machine techniques. Asian J Appl Sci2015; 8: 149–157.
283.
BoserBEGuyonIMVapnikVN. A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory, Pittsburgh, PA, 27–29 July 1992, pp. 144–152. New York: ACM.
284.
BagciAMAnsariRKhokharA, et al. Eye tracking using Markov models. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, Cambridge, 26 August 2004, vol. 3, pp. 818–821. New York: IEEE.
285.
HoodaRJoshiVShahM. A comprehensive review of approaches to detect fatigue using machine learning techniques. Chronic Dis Transl Med2022; 8: 26–35.
286.
MayJFBaldwinCL. Driver fatigue: the importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transp Res Part F: Traff Psych Behav2009; 12: 218–224.
287.
KörberMCingelAZimmermannM, et al. Vigilance decrement and passive fatigue caused by monotony in automated driving. Proc Manuf2015; 3: 2403–2409.
288.
ThiffaultPBergeronJ. Monotony of road environment and driver fatigue: a simulator study. Accid Anal Prev2003; 35: 381–391.
289.
BougardCEspiéSLarnaudieB, et al. Effects of time of day and sleep deprivation on motorcycle-driving performance. PLoS ONE2012; 7: e39735.
290.
PhilipPSagaspePMooreN, et al. Fatigue, sleep restriction and driving performance. Accid Anal Prev2005; 37: 473–478.
291.
PatelANHowardMDRoachSM, et al. Mental state assessment and validation using personalized physiological biometrics. Front Hum Neurosci2018; 12: 221.
292.
FoongRAngKKZhangZ, et al. An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue. J Neur Eng2019; 16: 056013.
293.
AlZu’biHSAl-NuaimyWAl-ZubiNS. EEG-based driver fatigue detection. In: 2013 sixth international conference on developments in eSystems engineering, Abu Dhabi, United Arab Emirates, 16–18 December 2013, pp. 111–114. New York: IEEE.
294.
YuZLiLZhangW, et al. An adaptive EEG feature extraction method based on stacked denoising autoencoder for mental fatigue connectivity. Neural Plast2021; 2021: 3965385.
295.
BelakhdarIKaanicheWDjmelR, et al. Detecting driver drowsiness based on single electroencephalography channel. In: 2016 13th international multi-conference on systems, signals & devices (SSD), Leipzig, 21–24 March 2016, pp. 16–21. New York: IEEE.
296.
MassozQLangohrTFrançoisC, et al. The ULg multimodality drowsiness database (called DROZY) and examples of use. In: 2016 IEEE winter conference on applications of computer vision (WACV), Lake Placid, NY, 7–10 March 2016, pp. 1–7. New York: IEEE.
297.
WengCHLaiYHLaiSH. Driver drowsiness detection via a hierarchical temporal deep belief network. In: CSChenJLuKKMa (eds) Asian conference on computer vision. Cham: Springer, 2017, pp. 117–133.
298.
ShiLZhengLJinD, et al. Assessment of combination of automated pupillometry and heart rate variability to detect driving fatigue. Front Public Health2022; 10: 828428.
299.
RogadoEGarciaJLBareaR, et al. Driver fatigue detection system. In: 2008 IEEE international conference on robotics and biomimetics, Beijing, China, 13–15 August 2016, pp. 1105–1110. New York: IEEE.
300.
YangCWangXMaoS. Unsupervised drowsy driving detection with RFID. IEEE Trans Vehicular Technol2020; 69: 8151–8163.
301.
ShakeelMFBajwaNAAnwaarAM, et al. Detecting driver drowsiness in real time through deep learning based object detection. In: IRojasGJoyaACatala (eds) International work-conference on artificial neural networks. Cham: Springer, 2019, pp. 283–296.
302.
SamieeSAzadiSKazemiR, et al. Data fusion to develop a driver drowsiness detection system with robustness to signal loss. Sensors2014; 14: 17832–17847.
303.
HashemiMFarahaniBFirouziF. Towards safer roads: a deep learning-based multimodal fatigue monitoring system. In: 2020 international conference on Omni-layer intelligent systems (COINS), Barcelona, Spain, 31 August 2020 – 02 September 2020, pp. 1–8. New York: IEEE.
304.
HuoXQZhengWLLuBL. Driving fatigue detection with fusion of EEG and forehead EOG. In: 2016 international joint conference on neural networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016, pp. 897–904. New York: IEEE.
305.
MarottaLBuurkeJHvan BeijnumBJF, et al. Towards machine learning-based detection of running-induced fatigue in real-world scenarios: evaluation of IMU sensor configurations to reduce intrusiveness. Sensors2021; 21: 3451.
306.
KullmannAAshmoreRCBravermanA, et al. Portable eye-tracking as a reliable assessment of oculomotor, cognitive and reaction time function: normative data for 18–45 year old. PLoS ONE2021; 16: e0260351.
307.
HancockPDesmondPAMatthewsG. Conceptualizing and defining fatigue. In: GMatthewsPADesmondPAHancock (eds) The handbook of operator fatigue. London: CRC Press, 2017, pp. 63–73.
308.
DevendorfARJacksonCTSunnquistM, et al. Defining and measuring recovery from myalgic encephalomyelitis and chronic fatigue syndrome: the physician perspective. Disabil Rehabil2019; 41: 158–165.
309.
HensonJS. Burnout or compassion fatigue: a comparison of concepts. Medsurg Nurs2020; 29: 77–81, 95.
310.
KelleyTL. Interpretation of educational measurements. Yonkers, NY: World Book Co, 1927.
