Cognitive Workload Analysis of Fighter Aircraft Pilots in Flight Simulator Environment

K. Mohanavelu; S. Poonguzhali; D. Ravi; Pushpendra K. Singh; Mistu Mahajabin; Ramachandran K.; Upendra Kumar Singh; Srinivasan Jayaraman

doi:10.14429/dsj.70.14539

K. Mohanavelu DRDO- Defence Bioenginnering & Electromedical Laboratory, Bengaluru - 560 093
S. Poonguzhali Centre for Medical Electronics, Anna University, Chennai - 600 025
D. Ravi DRDO-Defence Institute of Psychological Research Laboratory, Delhi- 110 054
Pushpendra K. Singh DRDO-Defence Institute of Psychological Research Laboratory, Delhi - 110 054
Mistu Mahajabin DRDO-Defence Institute of Psychological Research Laboratory, Delhi - 110 054, India
Ramachandran K. DRDO-Defence Institute of Psychological Research Laboratory, Delhi - 110 054, India
Upendra Kumar Singh DRDO- Defence Bioenginnering & Electromedical Laboratory, Bengaluru, India
Srinivasan Jayaraman Medical Device and Development, Tata Consultancy Services, Bengaluru - 560 066, India

DOI: https://doi.org/10.14429/dsj.70.14539

Keywords: Pilot cognitive workload, HRV, NASA-TLX, Fighter pilots, Flight simulator

Abstract

Maintaining and balancing an optimal level of workload is essential for completing the task productively. Fighter aircraft is one such example, where the pilot is loaded heavily both physically (due to G manoeuvering) and cognitively (handling multiple sensors, perceiving, processing and multi-tasking including communications and handling weapons) to fulfill the combat mission requirements. This cognitive demand needs to be analysed to understand the workload of fighter pilot. Objective of this study is to analyse dynamic workload of fighter pilots in a realistic high-fidelity flight simulator environment during different flying workload conditions. The various workload conditions are (a) normal visibility, (b) low visibility, (c) normal visibility with secondary task, and (d) low visibility with secondary task. Though, pilot’s flying performance score was good, the physiological measure like heart rate variability (HRV) features and subjective assessment (NASA-TLX) components are found to be statistically significant (p<0.05) between tasks. HRV features such as SD2, SDNN, VLF and total power are found to be significant at all task load conditions. The features LFnu and HFnu are able to differentiate the effect of low visibility and secondary cognitive task, which was imposed as increased task in this study. This result benefits to understand the pilot’s task and performance at each flying phase and their cognitive demands during dynamic workload using HRV, which could assist pilot’s training schedule in optimal way on simulators as well as in actual flight conditions.

Author Biographies

K. Mohanavelu, DRDO- Defence Bioenginnering & Electromedical Laboratory, Bengaluru - 560 093

Mr Mohanavelu K. obtained his BE (Instrumentation and Control) from University of Madras, Chennai and MTech (Biomedical Engineering) from IIT Madras. He is currently working as Scientist at DRDO- Defence Bioenginnering & Electromedical Laboratory, Bengaluru. His areas of research include wearable physiological monitoring, performance measurement and enhancement of military personnel, Telemedicine, brain computer interface, noninvasive brain stimulation and exoskeleton.

S. Poonguzhali, Centre for Medical Electronics, Anna University, Chennai - 600 025

Dr S. Poonguzhali obtained his BE (Electronics and Instrumentation Engineering) from Annamalai University, India and ME and PhD in Medical Electronics from College of Engineering, Guindy, Anna University, Chennai. She is currently working as Assistant Professor in Dept of ECE, College of Engineering, Guindy, Anna University, Chennai, India. Her areas of research include biomedical image processing, rehabilitation engineering and medical instrumentation.

D. Ravi, DRDO-Defence Institute of Psychological Research Laboratory, Delhi- 110 054

Dr D. Ravi obtained PhD in Psychometrics from Bharthiar University Coimbatore. He is currently working as scientist at DRDO-Defence Institute of Psychological Research Laboratory, Delhi. Currently he is working in the field of Human factor and Aviation psychology. His areas of research include application of machine learning in pilot mental workload estimation, pilot selection system and cognitive function in high altitude.

Pushpendra K. Singh, DRDO-Defence Institute of Psychological Research Laboratory, Delhi - 110 054

Mr Pushpendra Kumar Singh obtained his BE (Electronics & TC) from RJPV Bhopal with Honor, and MTech in Signal Processing (Electrical Engineering) from IIT Kanpur. He is currently working as scientist at DIPR, DRDO Delhi. His areas of research includes system design, machine learning, cognitive science (perception and decision making), cognitive load assessment for air force pilots and BCI.

Mistu Mahajabin, DRDO-Defence Institute of Psychological Research Laboratory, Delhi - 110 054, India

Ms Mistu Mahajabin obtained her BE (Computer Science and Engineering) from West Bengal University of Technology, Kolkata. She is currently working as a scientist at DRDO-Defence Institute of Psychological Research Laboratory, Delhi. Her areas of research include measuring psychological abilities through computerise test, text analysis using machine learning and big data analysis.
In the current study, she has designed secondary task, data collection during experiments and its analyses.

Ramachandran K., DRDO-Defence Institute of Psychological Research Laboratory, Delhi - 110 054, India

Dr K. Ramachandran, did his Post-Graduation in Applied Psychology from Bharathiar University, Coimbatore, M.Phil in Psychology from University of Madras and PhD in Psychology from University of Delhi. Currently working as Scientist ‘H’ & Director DRDO-Defence Institute of Psychological Research Laboratory, Delhi. He has acquired specialisation in the areas of experimental and human engineering, aviation psychology and environmental psychology.
In the current study he helped in reviewing experimental protocol, NASA-TLX questionnaire and reviewed inference manuscript.

Upendra Kumar Singh, DRDO- Defence Bioenginnering & Electromedical Laboratory, Bengaluru, India

Dr Upendra Kumar Singh did his MSc and MTech in computer Science from DAVV, Indore and obtained his doctorate from University of Hyderabad on soft computing. Currently working as Scientist ‘H’ & Director DRDO- Defence Bioenginnering & Electromedical Laboratory, Bengaluru.
In the current study, he helped in reviewing algorithms, results and manuscript.

Srinivasan Jayaraman, Medical Device and Development, Tata Consultancy Services, Bengaluru - 560 066, India

Dr Srinivasan Jayaraman has completed his PhD from IIT-Madras, India. Currently working as a lead Scientist at Medical device and development division– Bengaluru. After completing his doctorate, he worked as a Scientist at TCS Innovation Labs– Bengaluru (2008-2018). His research interests include cardiac computation model, human performance and behavioral modelling, ontology, AI, personalised diagnosis system, wearable devices, and human system interface.
In the current study, he is involved in extracting HRV features, inference and review on the manuscript.

References

Wiegmann, D. A. & Shappell, S.A. Human error analysis of commercial aviation accidents: Application of the human factors analysis and classification system (HFACS). Aviation, Space, Environ. Med., 2001, 72, 1006–1016.

Causes of fatal accidents by decade -statistic-http://www.planecrashinfo.com/cause.htm

Kotresh, T.M. & Prasad, A.S.K. Development and evaluation of flame retardant outer garment for anti G suit. Man-Made Textiles India., 1997, 40(5), 206–210.

Borghini, G.; Astolfi, L.; Vecchiato, G. & Mattia, D. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci. Biobehavioral Rev., 2014, 44, 58-75. https://doi.org/10.1016/j.neubiorev.2012.10.003.

Glenn, F. Wilson. An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int. J. Aviation Psychol., 2002, 12(1), 3-18. https://doi.org/10.1207/s15327108ijap1201_2.

Charles, L. R. & Jim, N. Measuring mental workload using physiological measures: A systematic review. Applied Ergonomics, 2018, 74, 221-232. https://doi.org/10.1016/j.apergo.2018.08.028.

Hart, S.G. & Staveland, E. L. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances Psychology, 1988, 52,139-183. https://doi.org/10.1016/S0166-4115(08)62386-9

Nagendra, H.; Kumar, V. & Mukherjee, S. Cognitive behavior evaluation based on physiological parameters among young healthy subjects with yoga as intervention. Computat. Math. Methods Med., 2015, Article ID 821061. https://doi.org/10.1155/2015/821061.

Lahtinen, M.M. T.; Koskelo, P. J.; Laitinen, T. & Leino, K. T. Heart rate and performance during combat missions in a flight simulator. Aviation, Space, Environ. Med., 2007, 78 (4), 387–391.

Mohanavelu, K.; Lamshe, R.; Poonguzhali, S.; Adalarasu, K. & Jagannath, M. Assessment of human fatigue during physical performance using physiological signals: A review. Biomed. Pharmacol. J., 2017, 10(4).

Mehler, B.; Reimer, B. & Wang, Y. A comparison of heart rate and heart rate variability indices in distinguishing single-task driving and driving under secondary cognitive workload. In Proceedings of the Sixth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 2011, 590–597. https://doi.org/10.17077/drivingassessment.1451

Mansikka, HP.; Virtanen, K.; Harris, D. & Simola, P. Fighter pilots’ heart rate, heart rate variation and performance during an instrument flight rules proficiency test. Applied Ergonomics., 2016, 56, 213–219. https://doi.org/10.1016/j.apergo.2016.04.006.

Zongmin, W.; Damin, Z.; Xiaoru, W.; Chen, L. & Huan, Z. A model for discrimination and prediction of mental workload of aircraft cockpit display interface. Chinese J. Aeronautics, 2014, 27 (5), 1070–1077. https://doi.org/10.1016/j.cja.2014.09.002.

Sharma, S.; Baijal, R. & Sinha, A. Mental work load assessment during different simulated instrument meteorological conditions in clouds and during dark night. Ind. J. Aerospace Med., 2012, 56(1), 11-20.

Jorna, P.G.A.M. Heart rate and workload variations in actual and simulated flight. Journal Ergonomics., 1993, 36(9), 1043–1054. https://doi.org/10.1080/00140139308967976.

Rubio, S.; Diaz, E.; Martin, J. & Puente, M.J. Evaluation of Subjective Mental Workload: A comparison of SWAT, NASA-TLX, and workload profile methods. Applied Psychology: Int. Rev., 2004, 53 (1), 61–86. https://doi.org/10.1111/j.1464-0597.2004.00161.x

Pandian, P.S.; Mohanavelu, K.; Safeer, K.P.; Kotresh, T.M.; Shakunthala, D.T.; Gopal, Parvati. & Padaki, V.C. Smart Vest: Wearable multi-parameter remote physiological Monitoring system. Med. Eng. Phy., 2008, 30, 466–477. https://doi.org/10.1016/j.medengphy.2007.05.014.

Jayaraman, S. & Kumar, K. Stress detection of IT professional using one minute ECG record. In 18th International Neuroscience and Biological Psychiatry Conference-Stress and Behavior, At New Orleans, LA, USA, 2012.

Sumitha, M.; Mohanavelu. K.; Singh, S.P. & Ravindran, G. Development of wearable HRV analysis monitor: A tool for critical care. In 2nd National conference on signal processing, communication and VLSI Design, 2010.

Shaffer, F. & Ginsberg, J.P. An overview of heart rate variability metrics and norms. Frontiers Public Health, 2017, 5, Article 258. https://doi.org/10.3389/fpubh.2017.00258

Malik, M. Heart rate variability standards of measurement, physiological interpretation, and clinical use. Circulation, 1996, 93. 1043-1065.

Hart, S.G. NASA-Task load index; 20 years later. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2006, 50, 904–908. https://doi.org/10.1177/154193120605000909.