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
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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. https://doi.org/10.1007/978-3-642-33409-2_14
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. https://doi.org/10.1109/MS.2017.3571580
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. https://doi.org/10.1016/j.apergo.2018.08.028
Cokorilo, O. (2013). Human factor modelling for fast-time simulations in aviation. Aircraft Engineering and Aerospace Technology, 85(5), 389–405. https://doi.org/10.1108/AEAT-07-2012-0120
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. https://doi.org/10.3390/s19061324
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. https://doi.org/10.20858/sjsutst.2017.95.4
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. https://doi.org/10.1063/1.5092100
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. https://doi.org/10.1080/10447318.2012.676538
Kilic, B. (2021). Fatigue among student pilots. Aerospace Medicine and Human Performance, 92(1), 20–24(5). https://doi.org/10.3357/AMHP.5631.2021
Kiliç, B., & Gümüş, E. (2020). Application of HFACS to the nighttime aviation accidents and incidents. Journal of Aviation, 4(2), 10–16. https://doi.org/10.30518/jav.740590
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. https://doi.org/10.3389/fnins.2020.00795
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. https://doi.org/10.1111/tops.12515
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. https://doi.org/10.1007/978-3-319-58472-0_3
Luximon, A., & Goonetilleke, R. (2011). Simplified subjective workload assessment technique. Ergonomics, 44(3), 229–243. https://doi.org/10.1080/00140130010000901
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). https://doi.org/10.2478/jok-2019-0006
Martins, A. (2016). A review of important cognitive concepts in aviation. Aviation, 20(2), 65–84. https://doi.org/10.3846/16487788.2016.1196559
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. https://doi.org/10.20858/sjsutst.2017.94.15
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). https://doi.org/10.1088/1757-899X/421/4/042060
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. https://doi.org/10.1016/S0001-4575(03)00014-9
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. https://doi.org/10.1111/j.1464-0597.2004.00161.x
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. https://doi.org/10.1177/1541931213571170
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. https://doi.org/10.1177/0018720820916326
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. https://doi.org/10.1007/978-3-662-43370-6_5
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). https://doi.org/10.1088/1741-2560/12/4/046020