Skip to main content

Research on Fatigue Assessment Method Under Long-Endurance Simulated Flight Missions

  • Conference paper
  • First Online:
HCI in Mobility, Transport, and Automotive Systems (HCII 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15817))

Included in the following conference series:

  • 210 Accesses

Abstract

Flight fatigue is a combined result of both physical and mental fatigue. Fatigue assessment methods primarily include subjective scales, and objective methods that analyze various physiological signals related to flight fatigue. These involve collecting and analyzing key physiological parameters, such as electrocardiogram (ECG), electromyogram (EMG), and comparing them with subjective evaluations to determine whether a pilot is experiencing fatigue. However, most studies on long-duration fatigue have focused on automotive driving, typically over periods of about 4 h. Given the significant differences between fighter jet operations, which involve extreme combat conditions and extended flight durations, it is necessary to conduct long-duration flight experiments lasting around 8 h. There is currently a lack of a comprehensive and effective system for evaluating pilot fatigue during long-duration flights in fighter jets. Using a single physiological signal to evaluate and predict pilot fatigue carries inherent errors and uncertainties. Research has increasingly focused on fusing multiple physiological signals to improve fatigue detection accuracy. This study, based on the ejection seats of China’s 4th-generation fighter jets, aims to assess both mental and physical fatigue in pilots after prolonged simulated flights, identify effective fatigue evaluation indicators, and develop a robust evaluation model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
€34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 69.54
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 87.73
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

') var buybox = document.querySelector("[data-id=id_"+ timestamp +"]").parentNode var buyingOptions = buybox.querySelectorAll(".buying-option") ;[].slice.call(buyingOptions).forEach(initCollapsibles) var buyboxMaxSingleColumnWidth = 480 function initCollapsibles(subscription, index) { var toggle = subscription.querySelector(".buying-option-price") subscription.classList.remove("expanded") var form = subscription.querySelector(".buying-option-form") var priceInfo = subscription.querySelector(".price-info") var buyingOption = toggle.parentElement if (toggle && form && priceInfo) { toggle.setAttribute("role", "button") toggle.setAttribute("tabindex", "0") toggle.addEventListener("click", function (event) { var expandedBuyingOptions = buybox.querySelectorAll(".buying-option.expanded") var buyboxWidth = buybox.offsetWidth ;[].slice.call(expandedBuyingOptions).forEach(function(option) { if (buyboxWidth buyboxMaxSingleColumnWidth) { toggle.click() } else { if (index === 0) { toggle.click() } else { toggle.setAttribute("aria-expanded", "false") form.hidden = "hidden" priceInfo.hidden = "hidden" } } }) } initialStateOpen() if (window.buyboxInitialised) return window.buyboxInitialised = true initKeyControls() })()

Institutional subscriptions

Similar content being viewed by others

References

  1. 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 

  2. 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 

  3. 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 

  4. 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 

  5. 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 

  6. 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 

  7. 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

  8. 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 

  9. 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 

  10. 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 

  11. 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

  12. 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 

  13. 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 

  14. 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 

  15. 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

  16. 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 

  17. 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 

  18. 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

  19. 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

  20. 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 

  21. 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

  22. 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 

  23. 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 

  24. 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 

  25. 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 

  26. 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 

  27. 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 

  28. 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 

  29. 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 

  30. 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 

  31. 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 

  32. 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 

  33. 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 

  34. 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 

  35. 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 

  36. 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 

  37. 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

  38. 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 

  39. 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 

  40. 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 

  41. 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 

  42. 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

Download references

Acknowledgments

This study was funded by National Natural Science Foundation of China (12202035).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiayi Bao .

Editor information

Editors and Affiliations

Ethics declarations

Disclosure of Interests

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, R., Guan, Y., Bao, J. (2025). Research on Fatigue Assessment Method Under Long-Endurance Simulated Flight Missions. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2025. Lecture Notes in Computer Science, vol 15817. Springer, Cham. https://doi.org/10.1007/978-3-031-92689-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-92689-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-92688-4

  • Online ISBN: 978-3-031-92689-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics