Analysis of the impact of task difficulty on the operatorʾs workload level


The widely held thesis is that the profession of pilot is one of the most difficult jobs to do. The task of the article was to analyse whether and how the difficulty of the performed task affects the pilot’s workload during the flight. The research was carried out using a flight simulator. During the simulator tests, the cognitive load measurements represented by the change in pilot pulse and concentration were used. A finger pulse oximeter was used for the first purpose. The second device was Mindwave Mobile which allows to measure level of pilot’s concentration and relaxation. The NASA-TLX questionnaire is used as a subjective method of operator’s workload assessment. The examined person assesses the level of his/her load, using six dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration level. Five research hypotheses were put forward and verified by the Friedman test. It has been shown that the level of difficulty of individual stages of the study is appropriately differentiated by pulse, concentration, relaxation, and subjective assessment of the respondents’ workload. It has been proved that pulse measurement, concentration, and relaxation levels, as well as subjective assessment of load levels, can be successfully used to assess the psychophysical condition of the operator.

Keyword : flight simulator, pilot workload, task difficulty, aviation, Friedman test

How to Cite
Galant-Gołębiewska, M., Mika, B., & Maciejewska, M. (2022). Analysis of the impact of task difficulty on the operatorʾs workload level. Aviation, 26(2), 72–78.
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May 26, 2022
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Biernacki, M., & Zieliński, P. (2010). Analiza psychometryczna polskiego przekładu narzędzia do subiektywnej oceny obciążenia NASA-TLX. Polski Przegląd Medycyny Lotniczej, 3, 219–239.

Boril, J., & Jalovecky, R. (2012). Experimental identification of pilot response using measured data from a flight simulator. In L. Iliadis, I. Maglogiannis, & H. Papadopoulos (Eds.), Artificial intelligence applications and innovations. AIAI 2012. IFIP Advances in Information and Communication Technology (Vol. 381). Springer.

CAE Oxford Aviation Academy. (2014). ATPL ground training series. Human performance and limitations. KHL Printing Co. Pte Ltd.

Cain, B. (2007). A review of the mental workload literature. Defence research & development. Canada, Human System Integration.

Cameron, N., Thomson, D. G., & Murray-Smith, D. J. (2003). Pilot modelling and inverse simulation for initial handling qualities assessment. Aeronautical Journal, 107(1074), 511–520.

Carver, J. C., Penzenstadler, B., Serebrenik, A., & Yamashita, A. (2017). The Human factor. IEEE Software, 34(5), 90–92.

Cekan, P., Korba, P., & Sabo, J. (2014). Human factor in aviation – models eliminating errors. In Transport Means – Proceedings of the International 18th International Conference on Transport Means (pp. 464–467). Kaunas University of Technology, Kaunas, Lithuania.

Charles, R., & Nixon, J. (2018). Measuring mental workload using physiological measures: A systematic review. Applied Ergonomics, 74, 221–232.

Cokorilo, O. (2013). Human factor modelling for fast-time simulations in aviation. Aircraft Engineering and Aerospace Technology, 85(5), 389–405.

Dehais, F., Duprès, A., Blum, S., Drougard, N., Scannella, S., Roy, R. N., & Lotte, F. (2019). Monitoring pilot’s mental workload using ERPs and spectral power with a six-dry-electrode EEG system in real flight conditions. Sensors, 19(6), 1324.

Galant, M., & Merkisz, J. (2017). Analysis of the possibilities of using EEG in assessing pilots’ psychophysical condition. Scientific Journal of Silesian University of Technology. Series Transport, 95, 39–46.

Galant, M., Nowak, M., Maciejewska, M., Kardach, M., & Łęgowik, A. (2019). Using the simulation technique to improve efficiency in general aviation. AIP Conference Proceedings, 2078, 020097.

Hertzum, M., & Holmegaard, K. (2012). Perceived time as a measure of mental workload: Effects of time constraints and task success. International Journal of Human-computer Interaction – IJHCI, 29(1), 26–39.

Kilic, B. (2021). Fatigue among student pilots. Aerospace Medicine and Human Performance, 92(1), 20–24(5).

Kiliç, B., & Gümüş, E. (2020). Application of HFACS to the nighttime aviation accidents and incidents. Journal of Aviation, 4(2), 10–16.

Klaproth, O., Vernaleken, Ch., Krol, L. R., Halbruegge, M., Zander, Th. M., & Russwinkel, N. (2020a). Tracing pilots’ situation assessment by neuroadaptive cognitive modeling. Frontiers in Neuroscience, 14, 795.

Klaproth, O. W., Halbruegge, M., Krol, L. R., Vernaleken, Ch., Zander, Th. O., & Russwinkel, N. (2020b). A neuroadaptive cognitive model for dealing with uncertainty in tracing pilots’ cognitive state. Topics in Cognitive Science, 12(3), 1012–1029.

Liu, W., Lu, Y., Huang, D., & Fu, Sh. (2017). An analysis of pilot’s workload evaluation based on time pressure and effort. In D. Harris (Ed.), EPCE 2017, Part I, LNAI, 10275, 32–41.

Luximon, A., & Goonetilleke, R. (2011). Simplified subjective workload assessment technique. Ergonomics, 44(3), 229–243.

Maciejewska, M., Galant, M., Kardach, M., & Fuć, P. (2019). Use of faultlessness indicator to rate human reliability in human – operating aircraft system. Journal of KONBiN, 49(2019).

Martins, A. (2016). A review of important cognitive concepts in aviation. Aviation, 20(2), 65–84.

Merkisz, J., Galant, M., & Bieda, M. (2017). Analysis of operating instrument landing system accuracy under simulated conditions, Scientific Journal of Silesian University of Technology. Series Transport, 94, 163–173.

Miszczak, A., & Walasek J. (2013). Techniki wyboru próby badawczej. Obronność – Zeszyty Naukowe Wydziału Zarządzania i Dowodzenia Akademii Obrony Narodowej, 2(6), 100–108.

Nowak, M., Jasiński, R., & Galant, M. (2018). Implementation of the LTO cycle in flight conditions using FNPT II MCC simulator. IOP Conference Series: Materials Science and Engineering, 421(4).

Patten, Ch., Kircher, A., Östlund, J., & Nilsson, L. (2004). Using mobile telephones: Cognitive workload and attention resource allocation. Accident Analysis and Prevention, 36(3), 341–350.

Rubio, S., Díaz, E., Martín, J., & Puente, J. (2004). Evaluation of subjective mental workload: A comparison of SWAT, NASA-TLX, and workload profile methods. Applied Psychology: An International Review, 53(1), 61–86.

Splawn, J., & Miller, M. (2013). Prediction of perceived workload from task performance and heart rate measures. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 778–782.

Vu, K.-P. L., Rorie, R. C., Fern, L., & Shively, R. J. (2020). Human factors contributions to the development of standards for displays of unmanned aircraft systems in support of detect-and-avoid. Human Factors, 62(4), 505–515.

Vijay Rao, D., & Balas-Timar, D. (2014). A soft computing approach to model human factors in air warfare simulation system. Innovations. In V. Balas, P. Koprinkova-Hristova, & L. Jain (Eds.). Innovations in Intelligent Machines – 5. Studies in Computational Intelligence (Vol. 561). Springer.

Yu, K., Prasad, I., Mir, H., Thakor, N., & Al-Nashash, H. (2015). Cognitive workload modulation through degraded visual stimuli: A single-trial EEG study. Journal of Neural Engineering, 12(4).