Pilots of ultra light aircraft have limited training resources, but with the use of low cost simulators it might be possible to train and test some parts of their training on the ground. The purpose of this paper is to examine possibility of stress inducement on a low cost flight simulator. Stress is assessed from electrocardiogram and respiration. Engine failure during flight served as a stress inducement stimuli. For one flight, pilots had access to an emergency navigation system. There were recorded some statistically significant changes in parameters regarding breathing frequency. Although no significant change was observed in ECG parameters, there appears to be an effect on respiration parameters. Physiological signals processed with analysis of variance suggest, that the moment of engine failure and approach for landing affected average breathing frequency. Presence of navigation interface does not appear to have a significant effect on pilots.

Department of Measurement Faculty of Electrical Engineering Czech Technical University Prague Technická 2 Prague 166 27 Czech Republic

Zobrazit více v PubMed

Gawron, V. Human Performance, Workload, and Situational Awareness Measures Handbook, Second Edition (Taylor & Francis, 2008).

Khatwa, R. Flight simulator evaluation of pilot performance with the runway awareness and advisory system (raas). In Digital Avionics Systems Conference, 2004. DASC 04. The 23rd, vol. 1, 5.D.2–51–11 vol.1 10.1109/DASC.2004.1391343 (2004).

Endsley MR, Bolstad CA. Individual differences in pilot situation awareness. The International Journal of Aviation Psychology. 1994;4:241–264. doi: 10.1207/s15327108ijap0403_3. DOI

Snow, M. P. & Reising, J. M. Effect of pathway-in-the-sky and synthetic terrain imagery on situation awareness in a simulated low-level ingress scenario. Tech. Rep., DTIC Document (1999).

Ishibashi, A. Situation awareness in the automated glass-cockpit. In Systems, Man, and Cybernetics, 1999. IEEE SMC ‘99 Conference Proceedings. 1999 IEEE International Conference on, vol. 3, 710–714 vol. 3 10.1109/ICSMC.1999.823315 (1999).

Borst C, Sjer F, Mulder M, van Paassen M, Mulder J. Ecological approach to support pilot terrain awareness after total engine failure. Journal Of Aircraft. 2008;45:159–171. doi: 10.2514/1.30214. DOI

Beringer D, Luke T, Quate A, Walters E. Helicopter pilot use of a see-through, head-mounted display with pathway guidance for visually guided flight: Observations of navigation behavior and obstacle avoidance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2009;53:26. doi: 10.1177/154193120905300107. DOI

Jamson SL, Jamson AH. The validity of a low-cost simulator for the assessment of the effects of in-vehicle information systems. Safety Science. 2010;48:1477–1483. doi: 10.1016/j.ssci.2010.07.008. DOI

Hjortskov N, et al. The effect of mental stress on heart rate variability and blood pressure during computer work. European Journal of Applied Physiology. 2004;92:84–89. doi: 10.1007/s00421-004-1055-z. PubMed DOI

Agency, E. A. S. Annual Safety Review 2012. Interactive Technologies (Publications Office of the European Union, 2013).

Matthews G, Desmond PA. Task-induced fatigue states and simulated driving performance. Quarterly Journal of Experimental Psychology: Section A. 2002;55:659–686. doi: 10.1080/02724980143000505. PubMed DOI

Hanson, E. K. Focus of attention and pilot error. In Proceedings of the 2004 symposium on Eye tracking research & applications, 60–60 (ACM 2004).

Liu Y-C, Wen M-H. Comparison of head-up display (hud) vs. head-down display (hdd): driving performance of commercial vehicle operators in taiwan. International Journal of Human-Computer Studies. 2004;61:679–697. doi: 10.1016/j.ijhcs.2004.06.002. DOI

Knabl, P. & Többen, H. Symbology development for a 3d conformal synthetic vision helmet-mounted display for helicopter operations in degraded visual environment. In Harris, D. (ed.) Engineering Psychology and Cognitive Ergonomics. Understanding Human Cognition, vol. 8019 of Lecture Notes in Computer Science, 232–241, 10.1007/978-3-642-39360-0_26 (Springer Berlin Heidelberg, 2013).

Frantis, P. Emergency and precautionary landing assistant. In Digital Avionics Systems Conference (DASC), 2011 IEEE/AIAA 30th, 6E2–1–6E2–6, 10.1109/DASC.2011.6096111 (2011).

Prinzel LJ, III, Arthur JJ, III, Kramer LJ, Bailey RE. Pathway concepts experiment for head-down synthetic vision displays. In Proc. SPIE. 2004;5424:11–22. doi: 10.1117/12.545580. DOI

Shen Y-F, Rahman Z, Krusienski D, Li J. A vision-based automatic safe landing-site detection system. Aerospace and Electronic Systems, IEEE Transactions on. 2013;49:294–311. doi: 10.1109/TAES.2013.6404104. DOI

Chang Y-H, Yeh C-H. Human performance interfaces in air traffic control. Applied Ergonomics. 2010;41:123–129. doi: 10.1016/j.apergo.2009.06.002. PubMed DOI

Prinzel, L. J. et al. Synthetic vision cfit experiments for ga and commercial aircraft: “a picture is worth a thousand lives”. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. vol. 47, 164–168 (SAGE Publications, 2003).

Miwa M, Tsuchiya T, Yonezawa S, Yokoyama N, Suzuki S. Real-time flight trajectory generation applicable to emergency landing approach. Transactions of the Japan Society for Aeronautical and Space Sciences. 2009;52:21–28. doi: 10.2322/tjsass.52.21. DOI

Lu A, Ding W, Li H. Multi-information based safe area step selection algorithm for uav’s emergency forced landing.(unmanned aerial vehicle) Journal of Software. 2013;8:995.

Xu, W. et al. Non-holonomic path planning of a free-floating space robotic system using genetic algorithms. Advanced Robotics22, 451–476, 10.1163/156855308X294680 (2008).

Uçan, F. & Altilar, D. T. Using genetic algorithms for navigation planning in dynamic environments. Applied Computational Intelligence & Soft Computing 1 (2012).

Chen TL, Pritchett AR. Development and evaluation of a cockpit decision-aid for emergency trajectory generation. Journal of Aircraft. 2001;38:935–943. doi: 10.2514/2.2856. DOI

Williams KW. Impact of aviation highway-in-the-sky displays on pilot situation awareness. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2002;44:18–27. doi: 10.1518/0018720024494801. PubMed DOI

Hannula M, Huttunen K, Koskelo J, Laitinen T, Leino T. Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator. Computers in Biology and Medicine. 2008;38:1163–1170. doi: 10.1016/j.compbiomed.2008.09.007. PubMed DOI

Wang R, Zhang J, Zhang Y, Wang X. Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model. Biomedical Signal Processing and Control. 2011;7:490–498. doi: 10.1016/j.bspc.2011.09.004. DOI

Ryu K, Myung R. Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic. International Journal of Industrial Ergonomics. 2005;35:991–1009. doi: 10.1016/j.ergon.2005.04.005. DOI

Lean Y, Shan F. Brief review on physiological and biochemical evaluations of human mental workload. Human Factors and Ergonomics in Manufacturing & Service Industries. 2012;22:177–187. doi: 10.1002/hfm.20269. DOI

Moon B, et al. Fuzzy systems to process ecg and eeg signals for quantification of the mental workload. Information Sciences. 2002;142:23–35. doi: 10.1016/S0020-0255(02)00155-X. DOI

Lei S, Roetting M. Influence of task combination on eeg spectrum modulation for driver workload estimation. Human Factors. 2011;53:168–179. doi: 10.1177/0018720811400601. PubMed DOI

Pedrotti M, et al. Automatic stress classification with pupil diameter analysis. International Journal of Human-Computer Interaction. 2014;30:220–236. doi: 10.1080/10447318.2013.848320. DOI

Karthikeyan, P., Murugappan, M. & Yaacob, S. A review on stress inducement stimuli for assessing human stress using physiological signals. In Signal Processing and its Applications (CSPA), 2011 IEEE 7th International Colloquium on, 420–425 10.1109/CSPA.2011.5759914 (2011).

Xiong W, Faes L, Ivanov PC. Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations. Phys. Rev. E. 2017;95:062114. doi: 10.1103/PhysRevE.95.062114. PubMed DOI PMC

Schmitt DT, Stein PK, Ivanov PC. Stratification pattern of static and scale-invariant dynamic measures of heartbeat fluctuations across sleep stages in young and elderly. IEEE Transactions on Biomedical Engineering. 2009;56:1564–1573. doi: 10.1109/TBME.2009.2014819. PubMed DOI PMC

Ivanov PC, et al. Sleep-wake differences in scaling behavior of the human heartbeat: Analysis of terrestrial and long-term space flight data. Europhysics Letters. 1999;48:594. doi: 10.1209/epl/i1999-00525-0. PubMed DOI

Karasik R, et al. Correlation differences in heartbeat fluctuations during rest and exercise. Phys. Rev. E. 2002;66:062902. doi: 10.1103/PhysRevE.66.062902. PubMed DOI

Kantelhardt JW, et al. Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments. Phys. Rev. E. 2002;65:051908. doi: 10.1103/PhysRevE.65.051908. PubMed DOI