Journals / Marine Science and Technology Bulletin / 2026 / Volume: 10 Issue: 3

Measuring Mental Workload and Heart Rate Variability of Officers During Different Navigation Conditions

Barış Özsever(Piri Reis University, Maritime Faculty, Department of Maritime Transportation Management Engineering, 34940, Istanbul, Turkey)0000-0003-4926-2477
Leyla Tavacıoğlu(Istanbul Technical University, Maritime Faculty, Department of Basic Sciences, 34940, Istanbul, Turkey)0000-0002-9919-8226
Pages
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Publication Date
2021-09-22
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study provides an example of predicting MWL for routine navigation operations by using physiological measures in maritime transportation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Mental workload (MWL) has a negative effect on the functional states of watchkeeping officers that ultimately causes collisions and groundings at sea.
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Key Findings & Data:This study was conducted in a bridge simulator with 11 participants.
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Conclusion & Impact:This study provides an example of predicting MWL for routine navigation operations by using physiological measures in maritime transportation.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Heart rate variability · Mental workload · NASA-TLX

Abstract

Mental workload (MWL) has a negative effect on the functional states of watchkeeping officers that ultimately causes collisions and groundings at sea. The aim of this study is to measure the MWL of officers during different navigation conditions. This study was conducted in a bridge simulator with 11 participants. Heart rate variability (HRV) measurements were taken during the 4 steps which have different difficulty levels and subjective assessments were taken at the end of each step by using NASA-TLX. The results of the measurements showed that different levels of navigation tasks caused significantly different levels of MWL and HRV values and MWL and HRV increased when task difficulty increased. Additionally, the correlation between MWL perceived by the participants and the heart rate variability values of the participants was found statistically significant. This study provides an example of predicting MWL for routine navigation operations by using physiological measures in maritime transportation.

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Cite This Article

Özsever, B., Tavacıoğlu, L. (2021). Measuring Mental Workload and Heart Rate Variability of Officers During Different Navigation Conditions. Marine Science and Technology Bulletin, Volume: 10 Issue: 3. https://doi.org/10.33714/masteb.976409

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References (31)

  1. Aimie-Salleh, N., Ghani, N. A. A., Hasanudin, N., & Shafie, S. N. S. (2019). Heart rate variability recording system using photoplethysmography sensor. In T. Aslanidis, (Ed.), Autonomic nervous system monitoring (pp. 29-43). IntechOpen.
  2. Akhtar, M. J., & Bouwer Utne, I. (2015). Common patterns in aggregated accident analysis charts from human fatigue-related groundings and collisions at sea. Maritime Policy & Management, 42(2), 186-206. https://doi.org/10.1080/03088839.2014.926032
  3. Alberdi, A., Aztiria, A., & Basarab, A. (2016). Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Journal of Biomedical Informatics, 59, 49-75. https://doi.org/10.1016/j.jbi.2015.11.007
  4. Backs, R. W., Navidzadeh, H. T., & Xu, X. (2000). Cardiorespiratory indices of mental workload during simulated air traffic control. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(13), 89-92. https://doi.org/10.1177/154193120004401323
  5. De Rivecourt, M., Kuperus, M., Post, W., & Mulder, L. (2008). Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight. Ergonomics, 51(9), 1295-1319. https://doi.org/10.1080/00140130802120267
  6. De Waard, D. (1996). The measurement of drivers’ mental workload. The Traffic Research Center VSC.
  7. Delaney, J., & Brodie, D. (2000). Effects of short-term psychological stress on the time and frequency domains of heart-rate variability. Perceptual and Motor Skills, 91(2), 515-524. https://doi.org/10.2466/pms.2000.91.2.515
  8. Embrey, D., Blackett, C., Marsden, P., & Peachey, J. (2006). Development of a human cognitive workload assessment tool. MCA Final Report, Lancashire.
  9. Fairclough, S. H., Venables, L., & Tattersall, A. (2005). The influence of task demand and learning on the psychophysiological response. International Journal of Psychophysiology, 56(2), 171-184. https://doi.org/10.1016/j.ijpsycho.2004.11.003
  10. Finsen, L., Søgaard, K., Jensen, C., Borg, V., & Christensen, H. (2001). Muscle activity and cardiovascular response during computer-mouse work with and without memory demands. Ergonomics, 44(14), 1312-1329. https://doi.org/10.1080/00140130110099065
  11. Fournier, L. R., Wilson, G. F., & Swain, C. R. (1999). Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: Manipulations of task difficulty and training. International Journal of Psychophysiology, 31(2), 129-145. https://doi.org/10.1016/S0167-8760(98)00049-X
  12. Gao, Q., Wang, Y., Song, F., Li, Z., & Dong, X. (2013). Mental workload measurement for emergency operating procedures in digital nuclear power plants. Ergonomics, 56(7), 1070-1085. https://doi.org/10.1080/00140139.2013.790483
  13. Gould, K. S., Røed, B. K., Saus, E.-R., Koefoed, V. F., Bridger, R. S., & Moen, B. E. (2009). Effects of navigation method on workload and performance in simulated high-speed ship navigation. Applied Ergonomics, 40(1), 103-114. https://doi.org/10.1016/j.apergo.2008.01.001
  14. Grabowski, M., & Sanborn, S. D. (2003). Human performance and embedded intelligent technology in safety-critical systems. International Journal of Human-Computer Studies, 58(6), 637-670. https://doi.org/10.1016/S1071-5819(03)00036-3
  15. Grech, M., Horberry, T., & Koester, T. (2008). Human factors in the maritime domain. 1st ed. CRC Press. Kahneman, D. (1973). Attention and effort (Vol. 1063). Prentice-Hall.
  16. Kurt, R. E., Khalid, H., Turan, O., Houben, M., Bos, J., & Helvacioglu, I. H. (2016). Towards human-oriented norms: Considering the effects of noise exposure on board ships. Ocean Engineering, 120, 101-107. https://doi.org/10.1016/j.oceaneng.2016.03.049
  17. Lean, Y., & Shan, F. (2012). Brief review on physiological and biochemical evaluations of human mental workload. Human Factors and Ergonomics in Manufacturing & Service Industries, 22(3), 177-187. https://doi.org/10.1002/hfm.20269
  18. Lehrer, P., Karavidas, M., Lu, S.-E., Vaschillo, E., Vaschillo, B., & Cheng, A. (2010). Cardiac data increase association between self-report and both expert ratings of task load and task performance in flight simulator tasks: An exploratory study. International Journal of Psychophysiology, 76(2), 80-87. https://doi.org/10.1016/j.ijpsycho.2010.02.006
  19. Louie, V. W., & Doolen, T. L. (2007). A study of factors that contribute to maritime fatigue. Marine Technology, 44(2), 82-92. https://doi.org/10.5957/mt1.2007.44.2.82
  20. Optical Pulse Sensor User Guide. (2016). Optical Pulse Sensor User Guide Revision 1.6. Shimmer.
  21. Orlandi, L., & Brooks, B. (2018). Measuring mental workload and physiological reactions in marine pilots: Building bridges towards redlines of performance. Applied Ergonomics, 69, 74-92. https://doi.org/10.1016/j.apergo.2018.01.005
  22. Özsever, B., & Tavacıoğlu, L. (2018). Analysing the effects of working period on psychophysiological states of seafarers. International Maritime Health, 69(2), 84-93. https://doi.org/10.5603/IMH.2018.0013
  23. Ramshur, J. T. (2010). Design, evaluation, and application of heart rate variability analysis software (HRVAS). [M.Sc. Thesis. University of Memphis].
  24. Robert, G., Hockey, J., Healey, A., Crawshaw, M., Wastell, D. G., & Sauer, J. (2003). Cognitive demands of collision avoidance in simulated ship control. Human Factors, 45(2), 252-265. https://doi.org/10.1518/hfes.45.2.252.27240
  25. Selvaraj, N., Jaryal, A., Santhosh, J., Deepak, K. K., & Anand, S. (2008). Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. Journal of Medical Engineering & Technology, 32(6), 479-484. https://doi.org/10.1080/03091900701781317
  26. Sharma, N., & Gedeon, T. (2012). Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer Methods and Programs in Biomedicine, 108(3), 1287-1301. https://doi.org/10.1016/j.cmpb.2012.07.003
  27. Splawn, J. M., & Miller, M. E. (2013). Prediction of perceived workload from task performance and heart rate measures. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 778-782. https://doi.org/10.1177/1541931213571170
  28. Veltman, J., & Gaillard, A. (1998). Physiological workload reactions to increasing levels of task difficulty. Ergonomics, 41(5), 656-669. https://doi.org/10.1080/001401398186829
  29. Wu, Y., Miwa, T., & Uchida, M. (2017). Using physiological signals to measure operator’s mental workload in shipping–an engine room simulator study. Journal of Marine Engineering & Technology, 16(2), 61-69. https://doi.org/10.1080/20464177.2016.1275496
  30. Yan, S., Wei, Y., & Tran, C. C. (2019). Evaluation and prediction mental workload in user interface of maritime operations using eye response. International Journal of Industrial Ergonomics, 71, 117-127. https://doi.org/10.1016/j.ergon.2019.03.002
  31. Young, M. S., & Stanton, N. A. (2002). Malleable attentional resources theory: a new explanation for the effects of mental underload on performance. Human Factors, 44(3), 365-375. https://doi.org/10.1518/0018720024497709