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Using eye tracking system for aircraft design – a flight simulator study

Abstract

The authors of this paper investigated applications of eye tracking in transport aircraft design evaluations. Piloted simulations were conducted for a complete flight profile including take-off, cruise and landing flight scenario using the transport aircraft flight simulator at CSIR-National Aerospace Laboratories. Thirty-one simulation experiments were carried out with three pilots/engineers while recording the ocular parameters and the flight data. Simulations were repeated for high workload conditions like flying with degraded visibility and during stall. Pilot’s visual scan behaviour and workload levels were analysed using ocular parameters; while comparing with the statistical deviations from the desired flight path. Conditions for fatigue were also recreated through long duration simulations and signatures for the same from the ocular parameters were assessed. Results from the study found correlation between the statistical inferences obtained from the ocular parameters with those obtained from the flight path deviations. The authors of this paper investigated an evaluator’s console that assists the designers or evaluators for better understanding of pilot’s attentional resource allocation.

Keyword : eye tracking, ocular parameters, visual scan, fatigue, IR cameras, cockpit display

How to Cite
Hebbar, P. A., Pashilkar, A. A., & Biswas, P. (2022). Using eye tracking system for aircraft design – a flight simulator study. Aviation, 26(1), 11–21. https://doi.org/10.3846/aviation.2022.16398
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Mar 22, 2022
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References

Anders, G. (2001). Pilot’s attention allocation during approach and landing: Eye and head tracking research in an A330 full flight simulator. In Proceedings of 11th International Symposium on Aviation psychology. Ohio State University Department of Aviation and Aeronautical Engineering, Columbus, OH.

Babu, M. D., JeevithaShree, D. V., Prabhakar, G., Saluja, K. P. S., Pashilkar, A., & Biswas, P. (2019). Estimating pilots cognitive load from ocular parameters through simulation and inflight studies. Journal of Eye Movement Research, 12(3), 3. https://doi.org/10.16910/jemr.12.3.3

Biella, M., Wies, M., Charles, R., Maille, N., Berberian, B., & Nixon, J. (2017). How eye tracking data can enhance human performance in tomorrow’s cockpit. Results from a flight simulation study in Future Sky Safety. In Proceedings of Joint AIAA and Royal Aeronautical Society Fall Conference on Modeling and Simulation for ATM (pp. 13–15). London, UK.

Biswas, P., & Langdon, P. (2015). Multimodal intelligent eye-gaze tracking system. International Journal of Human Computer Interaction, 31(4), 277–294. https://doi.org/10.1080/10447318.2014.1001301

Biswas, P., & Jeevithashree, D. V. (2018, 5 March). Eye gaze controlled MFD for military aviation. In International Conference on Intelligent User Interfaces, Proceedings IUI (pp. 79–89). Association for Computing Machinery, New York, United States. https://doi.org/10.1145/3172944.3172973

Funke, G., Greenlee, E., Carter, M., Dukes, A., Brown, R., & Menke, L. (2016). Which eye tracker is right for your research? Performance evaluation of several cost variant eye trackers. In Proceedings of Human Factors and Ergonomics Society Annual Meeting, 60(1), 1240–1244. https://doi.org/10.1177/1541931213601289

Haslbeck, A., Schubert, E., Gontar, P., & Bengler, K. (2012). The relationship between pilot’s manual flying skills and their visual behaviour: A flight simulator study using eye tracking. In Advances in Human Aspects of Aviation (pp. 561–568). CRC Press.

Hebbar, P. A., & Pashilkar, A. A. (2017). Pilot performance evaluation of simulated flight approach and landing manoeuvres using quantitative assessment tools. Sādhanā, 42(3), 405–415. https://doi.org/10.1007/s12046-017-0613-0

Hebbar, P. A., & Pashilkar, A. A. (2016). Application of quantitative measures for analysing aircraft handling qualities. Defence Science Journal, 66(1), 3–10. https://doi.org/10.14429/dsj.66.9196

Kramer, A. (1991). Physiological metrics of mental workload: A review of recent progress. In Multiple task performance (pp. 279–328). Taylor and Francis. https://doi.org/10.1201/9781003069447-14

Kret, M. E., & Sjak-Shie, E. E. (2019). Preprocessing pupil size data: Guidelines and code. Behavior Research Methods, 51, 1336–1342. https://doi.org/10.3758/s13428-018-1075-y

Lampton, A., & Klyde, D. H. (2012). Power frequency: A metric for analysing pilot in the loop flying tasks. Journal of Guidance, Control and Dynamics, 35(5), 1526–1537. https://doi.org/10.2514/1.55549

Latorella, K., Ellis, K. K., Lynn, W. A., Frasca, D., Burdette, D. W., Feigh, C. T., & Douglas, A. L. (2010). Dual oculometer system for aviation crew assessment. In Proceedings of Human Factors and Ergonomics Society, 54th Annual meeting. California, USA.

Li, W. C., Chiu, F. C., & Wu, K. J. (2012). The evaluation of pilot’s performance and mental workload by eye movement. In Proceedings of the 30th European Association for Aviation Psychology Conference. Sardinia, Italy.

Marshall, S. P. (2002). The index of cognitive activity: Measuring cognitive workload. In Proceedings of the IEEE 7th Conference on Human Factors and Power Plants (pp. 7–7). Scottsdale, USA. https://doi.org/10.1109/HFPP.2002.1042860

Neboshynsky, C. M. (2012). Expertise on cognitive workload and performance during navigation and target detection [Master’s Thesis]. Naval postgraduate school, California, USA.

Petkar, H., Dande, S., Yadav, R., Zeng, Y., & Nguyen, T. A. (2009). A pilot study to assess designer’s mental stress using eye gaze system and electroencephalogram. In Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (pp. 899–909). San Diego, California, USA. https://doi.org/10.1115/DETC2009-86542

Prabhakar, G., & Biswas, P. (2018). Detecting drivers’ cognitive load from saccadic intrusion. Transportation Research Part F: Traffic Psychology and Behaviour, 54, 63–78. https://doi.org/10.1016/j.trf.2018.01.017

Reiner, M., & Gelfeld, T. M. (2014). Estimating mental workload through event-related fluctuations of pupil area during a task in a virtual world. International Journal of Psychophysiology, 93(1), 38–44. https://doi.org/10.1016/j.ijpsycho.2013.11.002

Soares, V. C. G., Souza, J. K. S., Ginani, G. E., Pompeia, S., Tierra-Criollo, C. J., & Melgea, D. B. (2013). Identification of drowsiness and alertness conditions by means of Spectral F-Test applied to pupillometric signals. Journal of Physics: Conference Series 477, 012027. Tucuman, Argentina. https://doi.org/10.1088/1742-6596/477/1/012027

Spector, R. H. (1990). The pupils. In H. K. Walker, W. D. Hall, & J. W. Hurst (Eds.), Clinical methods: The history, physical, and laboratory examinations (3rd ed., Chapter 58). Butterworths. https://www.ncbi.nlm.nih.gov/books/NBK381/

Vansteenkiste, P., Cardon, G., Philippaerts, R., & Lenoir, M. (2015). Measuring dwell time percentage from head-mounted eye-tracking data – comparison of a frame by frame and fixation by fixation analysis. Ergonomics, 58(5), 712–721. https://doi.org/10.1080/00140139.2014.990524

Xu, J., Wang, Y., Chen, F., Choi, H., Li, G., Chen, S., & Hussain, S. (2011). Pupillary response based cognitive workload index under luminance and emotional changes. In CHI‘11 Extended Abstracts on Human Factors in Computing Systems (pp. 1627–1632). New York, USA. https://doi.org/10.1145/1979742.1979819