Caldwell, J.A.: Fatigue in aviation. Travel Med. Infect. Dis. 3, 85–96 (2005). https://doi.org/10.1016/j.tmaid.2004.07.008
Article
Google Scholar
Goode, J.H.: Are pilots at risk of accidents due to fatigue? J. Safety Res. 34, 309–313 (2003). https://doi.org/10.1016/S0022-4375(03)00033-1
Article
Google Scholar
Enoka, R.M., Duchateau, J.: Muscle fatigue: what, why and how it influences muscle function. J. Physiol. 586, 11–23 (2008). https://doi.org/10.1113/jphysiol.2007.139477
Article
Google Scholar
Sharpe, M.C.: A report–chronic fatigue syndrome: guidelines for research. J. R. Soc. Med. 84, 118–121 (1991). https://doi.org/10.1177/014107689108400224
Article
Google Scholar
Smets, E.M.A., Garssen, B., Cull, A., de Haes, J.: Application of the multidimensional fatigue inventory (MFI-20) in cancer patients receiving radiotherapy. Br. J. Cancer 73, 241–245 (1996). https://doi.org/10.1038/bjc.1996.42
Article
Google Scholar
Bendak, S., Rashid, H.S.J.: Fatigue in aviation: a systematic review of the literature. Int. J. Ind. Ergon. 76, 102928 (2020). https://doi.org/10.1016/j.ergon.2020.102928
Article
Google Scholar
Wingelaar-Jagt, Y.Q., Wingelaar, T.T., Riedel, W.J., Ramaekers, J.G.: Fatigue in aviation: Safety risks, preventive strategies and pharmacological interventions. Front. Physiol. 12 (2021). https://doi.org/10.3389/fphys.2021.712628
Lee, S., Kim, J.K.: Factors contributing to the risk of airline pilot fatigue. J. Air Transp. Manag. 67, 197–207 (2018). https://doi.org/10.1016/j.jairtraman.2017.12.009
Article
MathSciNet
Google Scholar
Gander, P.H., Rosekind, M.R., Gregory, K.B.: Flight crew fatigue VI: a synthesis. Aviat. Space Environ. Med. 69, B49-60 (1998)
Google Scholar
Li, Y., He, J.: A review of strategies to detect fatigue and sleep problems in aviation: insights from artificial intelligence. Arch Computat Methods Eng. (2024). https://doi.org/10.1007/s11831-024-10123-5
Article
Google Scholar
A systematic review on detection and prediction of driver drowsiness. Transp. Res. Interdisciplinary Perspect. 21, 100864 (2023). https://doi.org/10.1016/j.trip.2023.100864
Liu, Y., Lan, Z., Cui, J., Sourina, O., Müller-Wittig, W.: Inter-subject transfer learning for EEG-based mental fatigue recognition. Adv. Eng. Inform. 46, 101157 (2020). https://doi.org/10.1016/j.aei.2020.101157
Article
Google Scholar
Hu, X., Lodewijks, G.: Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: the value of differentiation of sleepiness and mental fatigue. J. Safety Res. 72, 173–187 (2020). https://doi.org/10.1016/j.jsr.2019.12.015
Article
Google Scholar
Lee, D.-H., Jeong, J.-H., Kim, K., Yu, B.-W., Lee, S.-W.: Continuous EEG decoding of pilots’ mental states using multiple feature block-based convolutional neural network. IEEE Access. 8, 121929–121941 (2020). https://doi.org/10.1109/ACCESS.2020.3006907
Article
Google Scholar
Ebrahimian, S., Nahvi, A., Tashakori, M., Salmanzadeh, H., Mohseni, O., Leppänen, T.: Multi-level classification of driver drowsiness by simultaneous analysis of ECG and respiration signals using deep neural networks. Int. J. Environ. Res. Public Health. 19, 10736 (2022). https://doi.org/10.3390/ijerph191710736
Huang, S., Li, J., Zhang, P., Zhang, W.: Detection of mental fatigue state with wearable ECG devices. Int. J. Med. Inf. 119, 39–46 (2018). https://doi.org/10.1016/j.ijmedinf.2018.08.010
Article
Google Scholar
Hartley, L.R., Arnold, P.K., Smythe, G., Hansen, J.: Indicators of fatigue in truck drivers. Appl. Ergon. 25, 143–156 (1994). https://doi.org/10.1016/0003-6870(94)90012-4
Article
Google Scholar
Riemersma, J.B.J., Sanders, A.F., Wildervanck, C., Gaillard, A.W.: Performance decrement during prolonged night driving. In: Mackie, R.R. (ed.) Vigilance: Theory, Operational Performance, and Physiological Correlates. pp. 41–58. Springer US, Boston, MA (1977). https://doi.org/10.1007/978-1-4684-2529-1_3
Costin, R., Rotariu, C., Pasarica, A.: Mental stress detection using heart rate variability and morphologic variability of EeG signals. In: 2012 International Conference and Exposition on Electrical and Power Engineering, pp. 591–596 (2012). https://doi.org/10.1109/ICEPE.2012.6463870
Malpas, S.C.: Neural influences on cardiovascular variability: possibilities and pitfalls. Am. J. Phys.-Heart Circulatory Phys. 282, H6–H20 (2002). https://doi.org/10.1152/ajpheart.2002.282.1.H6
Article
Google Scholar
Nizami, S., Green, James.R., Eklund, J.M., McGregor, C.: Heart disease classification through HRV analysis using parallel cascade identification and fast orthogonal search. In: 2010 IEEE International Workshop on Medical Measurements and Applications, pp. 134–139 (2010). https://doi.org/10.1109/MEMEA.2010.5480217
Boonnithi, S., Phongsuphap, S.: Comparison of heart rate variability measures for mental stress detection. In: 2011 Computing in Cardiology, pp. 85–88 (2011)
Google Scholar
Zhou, B., Chen, B., Shi, H., Xue, L., Ao, Y., Ding, L.: SEMG-based fighter pilot muscle fatigue analysis and operation performance research. Med. Novel Technol. Dev. 16, 100189 (2022). https://doi.org/10.1016/j.medntd.2022.100189
Article
Google Scholar
Rampichini, S., Vieira, T.M., Castiglioni, P., Merati, G.: Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: a review. Entropy 22, 529 (2020). https://doi.org/10.3390/e22050529
Article
Google Scholar
Skrzat, J.M., et al.: Use of surface electromyography to measure muscle fatigue in patients in an acute care hospital. Phys. Ther. 100, 897–906 (2020). https://doi.org/10.1093/ptj/pzaa035
Article
Google Scholar
Goubault, E., Martinez, R., Bouffard, J., Dowling-Medley, J., Begon, M., Dal Maso, F.: Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task. Ergonomics 65, 118–133 (2022). https://doi.org/10.1080/00140139.2021.1958013
Article
Google Scholar
Le, P., Mills, E.H.L., Weisenbach, C.A., Davis, K.G.: Neck muscle coactivation response to varied levels of mental workload during simulated flight tasks. Hum. Factors 66, 2041–2056 (2024). https://doi.org/10.1177/00187208231206324
Article
Google Scholar
Balasubramanian, V., Dutt, A., Rai, S.: Analysis of muscle fatigue in helicopter pilots. Appl. Ergon. 42, 913–918 (2011). https://doi.org/10.1016/j.apergo.2011.02.008
Article
Google Scholar
Wang, H.: Detection and alleviation of driving fatigue based on EMG and EMS/EEG using wearable sensor. EAI Endorsed Trans. Smart Cities. 1, e4–e4 (2016). https://doi.org/10.4108/eai.14-10-2015.2261628
Article
Google Scholar
Landis, C.: Determinants of the critical flicker-fusion threshold. Physiol. Rev. 34, 259–286 (1954). https://doi.org/10.1152/physrev.1954.34.2.259
Article
Google Scholar
Hindmarch, I.: Critical flicker fusion frequency (CFF): the effects of psychotropic compounds. Pharmacopsychiatry 15, 44–48 (2008). https://doi.org/10.1055/s-2007-1019549
Article
Google Scholar
Yamada, S., Miyake, S.: Effects of long term mental arithmetic on physiological parameters, subjective indices and task performances. J. UOEH 29, 27–38 (2007). https://doi.org/10.7888/juoeh.29.27
Article
Google Scholar
Jagannath, M., Balasubramanian, V.: Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator. Appl. Ergon. 45, 1140–1147 (2014). https://doi.org/10.1016/j.apergo.2014.02.001
Article
Google Scholar
Naeeri, S., Mandal, S., Kang, Z.: Analyzing pilots’ fatigue for prolonged flight missions: multimodal analysis approach using vigilance test and eye tracking. Proc. Hum. Fact. Ergon. Soc. Ann. Meeting. 63, 111–115 (2019). https://doi.org/10.1177/1071181319631092
Article
Google Scholar
Thiffault, P., Bergeron, J.: Monotony of road environment and driver fatigue: a simulator study. Accid. Anal. Prev. 35, 381–391 (2003). https://doi.org/10.1016/S0001-4575(02)00014-3
Article
Google Scholar
Zhou, F., et al.: Driver fatigue transition prediction in highly automated driving using physiological features. Expert Syst. Appl. 147, 113204 (2020). https://doi.org/10.1016/j.eswa.2020.113204
Article
Google Scholar
Sheridan, T.B., Meyer, J.E., Roy, S.H., Decker, K.S., Yanagishima, T., Kishi, Y.: Physiological and psychological evaluations of driver fatigue during long term driving. In: SAE International, Warrendale, PA (1991). https://doi.org/10.4271/910116
Rosa, E., et al.: Cognitive performance, fatigue, emotional, and physiological strains in simulated long-duration flight missions. Mil. Psychol. 34, 224–236 (2022). https://doi.org/10.1080/08995605.2021.1989236
Article
Google Scholar
Murugan, S., Selvaraj, J., Sahayadhas, A.: Detection and analysis: driver state with electrocardiogram (ECG). Phys. Eng. Sci. Med. 43, 525–537 (2020). https://doi.org/10.1007/s13246-020-00853-8
Article
Google Scholar
Lu, K., Sjörs Dahlman, A., Karlsson, J., Candefjord, S.: Detecting driver fatigue using heart rate variability: a systematic review. Accid. Anal. Prev. 178, 106830 (2022). https://doi.org/10.1016/j.aap.2022.106830
Article
Google Scholar
Cifrek, M., Medved, V., Tonković, S., Ostojić, S.: Surface EMG based muscle fatigue evaluation in biomechanics. Clin. Biomech. 24, 327–340 (2009). https://doi.org/10.1016/j.clinbiomech.2009.01.010
Article
Google Scholar
Gómez-Carmona, C.D., et al.: Lower-limb dynamics of muscle oxygen saturation during the back-squat exercise: effects of training load and effort level. J. Strength Conditioning Res. 34, 1227 (2020). https://doi.org/10.1519/JSC.0000000000003400