{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T10:39:26Z","timestamp":1774694366742,"version":"3.50.1"},"reference-count":152,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T00:00:00Z","timestamp":1565222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007034","name":"Thales Group","doi-asserted-by":"publisher","award":["0200315666"],"award-info":[{"award-number":["0200315666"]}],"id":[{"id":"10.13039\/501100007034","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005014","name":"Northrop Grumman","doi-asserted-by":"publisher","award":["0200317164"],"award-info":[{"award-number":["0200317164"]}],"id":[{"id":"10.13039\/100005014","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration\/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator\u2019s cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator\u2019s states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator\u2019s cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.<\/jats:p>","DOI":"10.3390\/s19163465","type":"journal-article","created":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T11:05:32Z","timestamp":1565262332000},"page":"3465","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Sensor Networks for Aerospace Human-Machine Systems"],"prefix":"10.3390","volume":"19","author":[{"given":"Nichakorn","family":"Pongsakornsathien","sequence":"first","affiliation":[{"name":"RMIT University\u2014School of Engineering, Bundoora, VIC 3083, Australia"}]},{"given":"Yixiang","family":"Lim","sequence":"additional","affiliation":[{"name":"RMIT University\u2014School of Engineering, Bundoora, VIC 3083, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4995-4166","authenticated-orcid":false,"given":"Alessandro","family":"Gardi","sequence":"additional","affiliation":[{"name":"RMIT University\u2014School of Engineering, Bundoora, VIC 3083, Australia"}]},{"given":"Samuel","family":"Hilton","sequence":"additional","affiliation":[{"name":"RMIT University\u2014School of Engineering, Bundoora, VIC 3083, Australia"}]},{"given":"Lars","family":"Planke","sequence":"additional","affiliation":[{"name":"RMIT University\u2014School of Engineering, Bundoora, VIC 3083, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3399-2291","authenticated-orcid":false,"given":"Roberto","family":"Sabatini","sequence":"additional","affiliation":[{"name":"RMIT University\u2014School of Engineering, Bundoora, VIC 3083, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2110-7021","authenticated-orcid":false,"given":"Trevor","family":"Kistan","sequence":"additional","affiliation":[{"name":"THALES Australia, WTC North Wharf, Melbourne, VIC 3000, Australia"}]},{"given":"Neta","family":"Ezer","sequence":"additional","affiliation":[{"name":"Northrop Grumman Corporation, 1550 W. Nursery Rd, Linthicum Heights, MD 21090, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,8]]},"reference":[{"key":"ref_1","unstructured":"Blockley, R., and Shyyeds, W. (2016). UAS in the Terminal Area: Challenges and Opportunities. Encyclopedia of Aerospace Engineering, John Wiley."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.paerosci.2018.10.006","article-title":"Space traffic management: Towards safe and unsegregated space transport operations","volume":"105","author":"Hilton","year":"2019","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.paerosci.2018.05.002","article-title":"Avionics Human-Machine Interfaces and Interactions for Manned and Unmanned Aircraft","volume":"102","author":"Lim","year":"2018","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kistan, T., Gardi, A., and Sabatini, R. (2018). Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification. Aerospace, 5.","DOI":"10.3390\/aerospace5040103"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.knosys.2016.08.031","article-title":"Cognitive pilot-aircraft interface for single-pilot operations","volume":"112","author":"Liu","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1007\/s10846-017-0648-9","article-title":"Cognitive Human-Machine Interfaces and Interactions for Unmanned Aircraft","volume":"91","author":"Lim","year":"2018","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.apergo.2018.08.028","article-title":"Measuring mental workload using physiological measures: A systematic review","volume":"74","author":"Charles","year":"2019","journal-title":"Appl. Ergon."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lim, Y., Gardi, A., Ramasamy, S., Vince, J., Pongracic, H., Kistan, T., and Sabatini, R. (2017, January 17\u201321). A novel simulation environment for cognitive human factors engineering research. Proceedings of the 2017 IEEE\/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, USA.","DOI":"10.1109\/DASC.2017.8102126"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.measurement.2019.03.032","article-title":"Experimental characterisation of eye-tracking sensors for adaptive human-machine systems","volume":"140","author":"Lim","year":"2019","journal-title":"Measurement"},{"key":"ref_10","unstructured":"Gilland, J. (2008). Driving, Eye-Tracking and Visual Entropy: Exploration of Age and Task Effects, University of South Dakota."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dehais, F., Peysakhovich, V., Scannella, S., Fongue, J., and Gateau, T. (2015, January 18\u201323). \u201cAutomation Surprise\u201d in Aviation. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea.","DOI":"10.1145\/2702123.2702521"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1518\/155534307X255627","article-title":"A random glance at the flight deck: Pilots\u2019 scanning strategies and the real-time assessment of mental workload","volume":"1","author":"Camilli","year":"2007","journal-title":"J. Cogn. Eng. Decis. Mak."},{"key":"ref_13","unstructured":"Harris, R.L., Glover, B.J., and Spady, A.A. (1986). Analytical Techniques of Pilot Scanning Behavior and Their Application."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/B978-044451020-4\/50031-1","article-title":"Eye tracking in human-computer interaction and usability research: Ready to deliver the promises","volume":"2","author":"Jacob","year":"2003","journal-title":"Mind Eye"},{"key":"ref_15","unstructured":"Glaholt, M.G. (2014). Eye Tracking in the Cockpit: A Review of the Relationships between Eye Movements and the Aviator\u2019s Cognitive State."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jessee, M.S. (2010). Examining the Convergent and Discriminant Validity of Visual and Mental Workload Using Ocular Activity Variables, Army Research Laboratory.","DOI":"10.21236\/AD1013150"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Salvucci, D.D., and Goldberg, J.H. (2000, January 6\u20138). Identifying fixations and saccades in eye-tracking protocols. Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, Palm Beach Gardens, FL, USA.","DOI":"10.1145\/355017.355028"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1177\/0018720811411297","article-title":"Evaluation of eye metrics as a detector of fatigue","volume":"53","author":"McKinley","year":"2011","journal-title":"Hum. Factors J. Hum. Factors Ergon. Soc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Trutschel, U., Sirois, B., Sommer, D., Golz, M., and Edwards, D. (2011, January 27\u201330). PERCLOS: An alertness measure of the past. Proceedings of the Sixth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Olympic Valley-Lake Tahoe, CA, USA.","DOI":"10.17077\/drivingassessment.1394"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sommer, D., and Golz, M. (September, January 31). Evaluation of PERCLOS based current fatigue monitoring technologies. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Buenos Aires, Argentina.","DOI":"10.1109\/IEMBS.2010.5625960"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s11768-010-8043-0","article-title":"A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue","volume":"8","author":"Zhang","year":"2010","journal-title":"J. Control Theory Appl."},{"key":"ref_22","unstructured":"Holmqvist, K., Nystr\u00f6m, M., Andersson, R., Dewhurst, R., Jarodzka, H., and Van de Weijer, J. (2011). Eye Tracking: A Comprehensive Guide to Methods and Measures, OUP Oxford."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"16495","DOI":"10.1109\/ACCESS.2017.2735633","article-title":"Performance Evaluation Methods in Consumer Platforms","volume":"5","author":"Kar","year":"2017","journal-title":"IEEE Access"},{"key":"ref_24","unstructured":"Ashley EA, N.J. (2004). Cardiology Explained, Remedica."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"258","DOI":"10.3389\/fpubh.2017.00258","article-title":"An Overview of Heart Rate Variability Metrics and Norms","volume":"5","author":"Shaffer","year":"2017","journal-title":"Front. Public Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","article-title":"Heart rate variability standards of measurement, physiological interpretation, and clinical use","volume":"17","author":"Malik","year":"1996","journal-title":"Eur. Heart J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/10.959330","article-title":"Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?","volume":"48","author":"Brennan","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/1091367X.2011.615671","article-title":"Reliability and Validity of the Zephyr BioHarness to Measure Respiratory Responses to Exercise","volume":"15","author":"Hailstone","year":"2011","journal-title":"Meas. Phys. Educ. Exerc. Sci."},{"key":"ref_29","first-page":"400","article-title":"Bioharness multivariable monitoring device: Part I: Validity","volume":"11","author":"James","year":"2012","journal-title":"J. Sports Sci. Med."},{"key":"ref_30","first-page":"409","article-title":"Bioharness Multivariable Monitoring Device: Part II: Reliability","volume":"11","author":"Johnstone","year":"2012","journal-title":"J. Sports Sci. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1519\/JSC.0000000000001842","article-title":"Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test and Recovery","volume":"33","author":"Nazari","year":"2019","journal-title":"J. Strength Cond. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1249\/MSS.0b013e318184a4b1","article-title":"Validity and Reliability of Short-Term Heart-Rate Variability from the Polar S810","volume":"41","author":"Nunan","year":"2009","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.apergo.2016.04.006","article-title":"Fighter pilots\u2019 heart rate, heart rate variation and performance during an instrument flight rules proficiency test","volume":"56","author":"Mansikka","year":"2016","journal-title":"Appl. Ergon."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8146809","DOI":"10.1155\/2016\/8146809","article-title":"Respiratory changes in response to cognitive load: A systematic review","volume":"2016","author":"Grassmann","year":"2016","journal-title":"Neural Plast."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.apergo.2015.07.009","article-title":"Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study","volume":"52","author":"Fallahi","year":"2016","journal-title":"Appl. Ergon."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"862","DOI":"10.3357\/ASEM.2531.2009","article-title":"Heart Rate Variability in Novice Pilots During and After a Multi-Leg Cross-Country Flight","volume":"80","author":"Sauvet","year":"2009","journal-title":"Aviat. Space Environ. Med."},{"key":"ref_37","unstructured":"Backs, R.W., Navidzadeh, H.T., and Xu, X. (August, January 30). Cardiorespiratory Indices of Mental Workload during Simulated Air Traffic Control. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, San Diego, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1111\/j.1469-8986.2010.01043.x","article-title":"Sigh rate and respiratory variability during mental load and sustained attention","volume":"48","author":"Vlemincx","year":"2010","journal-title":"Psychophysiology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"889","DOI":"10.3389\/fnhum.2013.00889","article-title":"Neuroergonomics: A review of applications to physical and cognitive work","volume":"7","author":"Mehta","year":"2013","journal-title":"Front. Hum. Neurosci."},{"key":"ref_40","unstructured":"Niedermeyer, E., and da Silva, F.L. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams & Wilkins."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MEMB.2006.1657788","article-title":"Functional near-infrared spectroscopy","volume":"25","author":"Bunce","year":"2006","journal-title":"IEEE Eng. Med. Boil. Mag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/nrn2795","article-title":"Neuromarketing: The hope and hype of neuroimaging in business","volume":"11","author":"Ariely","year":"2010","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0167-8760(96)00057-8","article-title":"Memory processes, brain oscillations and EEG synchronization","volume":"24","author":"Klimesch","year":"1996","journal-title":"Int. J. Psychophysiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1111\/1469-8986.00046","article-title":"Functional transcranial Doppler sonography as a tool in psychophysiological research","volume":"40","author":"Duschek","year":"2003","journal-title":"Psychophysiology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1016\/j.neuroimage.2012.03.049","article-title":"A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application","volume":"63","author":"Ferrari","year":"2012","journal-title":"Neuroimage"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1023\/A:1026412811036","article-title":"Transcranial Doppler ultrasonography monitoring of cerebral hemodynamics during performance of cognitive tasks: A review","volume":"10","author":"Stroobant","year":"2000","journal-title":"Neuropsychol. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1006\/nimg.1998.0369","article-title":"The variability of human, BOLD hemodynamic responses","volume":"8","author":"Aguirre","year":"1998","journal-title":"Neuroimage"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.ijpsycho.2015.05.004","article-title":"Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view","volume":"97","author":"Burle","year":"2015","journal-title":"Int. J. Psychophysiol."},{"key":"ref_49","first-page":"225","article-title":"The Evaluation of BCI and PEBL-based Attention Tests","volume":"15","author":"Katona","year":"2018","journal-title":"Acta Polytech. Hung."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1109\/TE.2016.2558163","article-title":"A Brain\u2013Computer Interface Project Applied in Computer Engineering","volume":"59","author":"Katona","year":"2016","journal-title":"IEEE Trans. Educ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Borghini, G., Aric\u00f2, P., Di Flumeri, G., Salinari, S., Colosimo, A., Bonelli, S., Napoletano, L., Ferreira, A., and Babiloni, F. (2015, January 25\u201329). Avionic technology testing by using a cognitive neurometric index: A study with professional helicopter pilots. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319804"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1518\/001872006779166280","article-title":"Comparison of a brain-based adaptive system and a manual adaptable system for invoking automation","volume":"48","author":"Bailey","year":"2006","journal-title":"Hum. Factors J. Hum. Factors Ergon. Soc."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.neuroimage.2011.07.094","article-title":"Cross-subject workload classification with a hierarchical Bayes model","volume":"59","author":"Wang","year":"2012","journal-title":"NeuroImage"},{"key":"ref_54","first-page":"220","article-title":"Percieved Mental Workload in a Simulated Task: Psychophysiological Evidence","volume":"38","author":"Saha","year":"2012","journal-title":"J. Indian Acad. Appl. Psychol."},{"key":"ref_55","first-page":"344","article-title":"EEG and ECG changes during simulator operation reflect mental workload and vigilance","volume":"76","author":"Dussault","year":"2005","journal-title":"Aviat. Space Environ. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0167-8760(94)90041-8","article-title":"Multiband topographic EEG analysis of a simulated visuomotor aviation task","volume":"16","author":"Sterman","year":"1994","journal-title":"Int. J. Psychophysiol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/0301-0511(95)05167-8","article-title":"Psychophysiological responses to changes in workload during simulated air traffic control","volume":"42","author":"Brookings","year":"1996","journal-title":"Biol. Psychol."},{"key":"ref_58","first-page":"360","article-title":"A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight","volume":"69","author":"Hankins","year":"1998","journal-title":"Aviat. Space Environ. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S0167-8760(98)00049-X","article-title":"Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: Manipulations of task difficulty and training","volume":"31","author":"Fournier","year":"1999","journal-title":"Int. J. Psychophysiol."},{"key":"ref_60","unstructured":"Mann, C.A., Kaiser, D.A., and Sterman, M.B. (1992, January 4\u20136). Quantitative EEG patterns of differential in-flight workload. Proceedings of the Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992), Houston, TX, USA."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.ergon.2005.04.005","article-title":"Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic","volume":"35","author":"Ryu","year":"2005","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_62","first-page":"35","article-title":"EEG based cognitive workload classification during NASA MATB-II multitasking","volume":"3","author":"Chandra","year":"2015","journal-title":"Int. J. Cogn. Res. Sci. Eng. Educ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1177\/0018720813476883","article-title":"Co-adaptive aiding and automation enhance operator performance","volume":"55","author":"Christensen","year":"2013","journal-title":"Hum. Factors J. Hum. Factors Ergon. Soc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1518\/001872007X249875","article-title":"Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding","volume":"49","author":"Wilson","year":"2007","journal-title":"Hum. Factors"},{"key":"ref_65","unstructured":"Wilson, G.F., Russell, C.A., Monnin, J.W., Estepp, J., and Christensen, J.C. (October, January 27). How does day-to-day variability in psychophysiological data affect classifier accuracy?. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, San Francisco, CA, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/10255810213481","article-title":"Feature selection for predicting pilot mental workload: A feasibility study","volume":"4","author":"East","year":"2002","journal-title":"Int. J. Smart Eng. Syst. Des."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/TSMCA.2002.807036","article-title":"Selection of input features across subjects for classifying crewmember workload using artificial neural networks","volume":"32","author":"Laine","year":"2002","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"McDonald, N.J., and Soussou, W. (September, January 30). Quasar\u2019s qstates cognitive gauge performance in the cognitive state assessment competition 2011. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091614"},{"key":"ref_69","first-page":"116","article-title":"Neurophysiologic monitoring of mental workload and fatigue during operation of a flight simulator","volume":"5797","author":"Smith","year":"2005","journal-title":"Def. Secur."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1518\/001872001775898287","article-title":"Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction","volume":"43","author":"Gevins","year":"2001","journal-title":"Hum. Factors J. Hum. Factors Ergon. Soc."},{"key":"ref_71","first-page":"539","article-title":"Adaptive automation triggered by EEG-based mental workload index: A passive brain-computer interface application in realistic air traffic control environment","volume":"10","author":"Borghini","year":"2016","journal-title":"Front. Hum. Neurosci."},{"key":"ref_72","first-page":"18","article-title":"Air-traffic-controllers (ATCO): Neurophysiological analysis of training and workload","volume":"12","author":"Borghini","year":"2015","journal-title":"Ital. J. Aerosp. Med."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Aric\u00f2, P., Borghini, G., Di Flumeri, G., Colosimo, A., Graziani, I., Imbert, J.-P., Granger, G., Benhacene, R., Terenzi, M., and Pozzi, S. (2015, January 25\u201329). Reliability over time of EEG-based mental workload evaluation during Air Traffic Management (ATM) tasks. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7320063"},{"key":"ref_74","unstructured":"Borghini, G., Aric\u1f78, P., Graziani, I., Salinari, S., Babiloni, F., Imbert, J.P., Granger, G., Benhacene, R., Napoletano, L., and Terenzi, M. (2014, January 25\u201327). Analysis of neurophysiological signals for the training and mental workload assessment of ATCos. Proceedings of the SESAR 2014, 4th SESAR Innovation Days, Madrid, Spain."},{"key":"ref_75","unstructured":"Poythress, M., Russell, C., Siegel, S., Tremoulet, P.D., Craven, P.L., Berka, C., Levendowski, D.J., Chang, D., Baskin, A., and Champney, R. (2006, January 15\u201320). Correlation between Expected Workload and EEG Indices of Cognitive Workload and Task Engagement. Proceedings of the 2nd Annual Augmented Cognition International Conference, San Francisco, CA, USA."},{"key":"ref_76","first-page":"90","article-title":"Evaluation of an EEG workload model in an Aegis simulation environment","volume":"5797","author":"Berka","year":"2005","journal-title":"Def. Secur."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/0301-0511(95)05116-3","article-title":"Biocybernetic system evaluates indices of operator engagement in automated task","volume":"40","author":"Pope","year":"1995","journal-title":"Biol. Psychol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0301-0511(99)00002-2","article-title":"Evaluation of an adaptive automation system using three EEG indices with a visual tracking task","volume":"50","author":"Freeman","year":"1999","journal-title":"Biol. Psychol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1207\/S15327108IJAP1004_6","article-title":"A closed-loop system for examining psychophysiological measures for adaptive task allocation","volume":"10","author":"Prinzel","year":"2000","journal-title":"Int. J. Aviat. Psychol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ijpsycho.2004.11.003","article-title":"The influence of task demand and learning on the psychophysiological response","volume":"56","author":"Fairclough","year":"2005","journal-title":"Int. J. Psychophysiol."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Senoussi, M., Verdiere, K.J., Bovo, A., Chanel, C.P.C., Roy, R.N., and Dehais, F. (2017, January 5\u20138). Pre-stimulus antero-posterior EEG connectivity predicts performance in a UAV monitoring task. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122770"},{"key":"ref_82","unstructured":"Zhang, G., Wang, W., Pepe, A., Xu, R., Schnell, T., Anderson, N., Heitkamp, D., Li, J., Li, F., and McKenzie, F. (2012, January 11). A systematic approach for real-time operator functional state assessment. Proceedings of the MODSIM World 2011 Conference and Expo., Virginia Beach, VA, USA."},{"key":"ref_83","unstructured":"Zhang, G., Xu, R., Wang, W., Pepe, A.A., Li, F., Li, J., McKenzie, F., Schnell, T., Anderson, N., and Heitkamp, D. (2012). Model Individualization for Real-Time Operator Functional State Assessment. Advances in Human Aspects of Aviation, CRC Press."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Harrivel, A.R., Stephens, C.L., Milletich, R.J., Heinich, C.M., Last, M.C., Napoli, N.J., Abraham, N., Prinzel, L.J., Motter, M.A., and Pope, A.T. (,  2017). Prediction of cognitive states during flight simulation using multimodal psychophysiological sensing. Proceedings of the AIAA Information Systems-AIAA Infotech, Grapevine, TX, USA.","DOI":"10.2514\/6.2017-1135"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1207\/s15327590ijhc1702_3","article-title":"Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset","volume":"17","author":"Berka","year":"2004","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"\u00c7ak\u0131r, M.P., \u015eenyi\u011fit, A.M., Akay, D.M., Ayaz, H., and \u0130\u015fler, V. (2012, January 11\u201314). Evaluation of UAS Camera Operator Interfaces in a Simulated Task Environment: An Optical Brain Imaging Approach. Proceedings of the International Conference on Brain Inspired Cognitive Systems, Shenyang, China.","DOI":"10.1007\/978-3-642-31561-9_7"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"\u00c7ak\u0131r, M.P., Vural, M., Ko\u00e7, S.\u00d6., Tokta\u015f, A., Schmorrow, D.D., and Fidopiastis, C.M. (2016, January 17\u201322). Real-Time Monitoring of Cognitive Workload of Airline Pilots in a Flight Simulator with fNIR Optical Brain Imaging Technology. Proceedings of the International Conference on Augmented Cognition, Toronto, ON, Canada.","DOI":"10.1007\/978-3-319-39955-3_14"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.bbr.2013.10.042","article-title":"Using near infrared spectroscopy and heart rate variability to detect mental overload","volume":"259","author":"Durantin","year":"2014","journal-title":"Behav. Brain Res."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Harrison, J., Izzetoglu, K., Ayaz, H., Willems, B., Hah, S., Woo, H., Shewokis, P.A., Bunce, S.C., and Onaral, B. (2013, January 21\u201326). Human performance assessment study in aviation using functional near infrared spectroscopy. Proceedings of the International Conference on Augmented Cognition, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-642-39454-6_46"},{"key":"ref_90","unstructured":"Ahlstrom, U., and Dworsky, M. (2012). Effects of Weather Presentation Symbology on General Aviation Pilot Behavior, Workload, and Visual Scanning."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s10846-010-9507-7","article-title":"Optical brain imaging to enhance UAV operator training, evaluation, and interface development","volume":"61","author":"Menda","year":"2011","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Ayaz, H., Willems, B., Bunce, S., Shewokis, P.A., Izzetoglu, K., Hah, S., Deshmukh, A., and Onaral, B. (2011, January 9\u201314). Estimation of Cognitive Workload during Simulated Air Traffic Control Using Optical Brain Imaging Sensors. Proceedings of the International Conference on Foundations of Augmented Cognition, Orlando, FL, USA.","DOI":"10.1007\/978-3-642-21852-1_63"},{"key":"ref_93","unstructured":"Afergan, D., Peck, E.M., Solovey, E.T., Jenkins, A., Hincks, S.W., Brown, E.T., Chang, R., and Jacob, R.J. (May, January 26). Dynamic difficulty using brain metrics of workload. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, Toronto, Canada."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"216","DOI":"10.3389\/fnhum.2016.00216","article-title":"Into the Wild: Neuroergonomic Differentiation of Hand-Held and Augmented Reality Wearable Displays during Outdoor Navigation with Functional Near Infrared Spectroscopy","volume":"10","author":"McKendrick","year":"2016","journal-title":"Front. Hum. Neurosci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1207\/s15327590ijhc1702_6","article-title":"Functional optical brain imaging using near-infrared during cognitive tasks","volume":"17","author":"Izzetoglu","year":"2004","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3389\/fnhum.2018.00006","article-title":"Detecting Pilot\u2019s Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario","volume":"12","author":"Roy","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"707","DOI":"10.3389\/fnhum.2015.00707","article-title":"Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight","volume":"9","author":"Durantin","year":"2016","journal-title":"Front. Hum. Neurosci."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Gateau, T., Durantin, G., Lancelot, F., Scannella, S., and Dehais, F. (2015). Real-time state estimation in a flight simulator using fNIRS. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0121279"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"871","DOI":"10.3389\/fnhum.2013.00871","article-title":"Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: Empirical examples and a technological development","volume":"7","author":"Ayaz","year":"2013","journal-title":"Front. Hum. Neurosci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"219","DOI":"10.3389\/fnhum.2016.00219","article-title":"Exploring Neuro-Physiological Correlates of Drivers\u2019 Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data","volume":"10","author":"Ahn","year":"2016","journal-title":"Front. Hum. Neurosci."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2560\/12\/3\/031001","article-title":"EEG artifact removal\u2014State-of-the-art and guidelines","volume":"12","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"630649","DOI":"10.1155\/2010\/630649","article-title":"Improvement of EEG Signal Acquisition: An Electrical Aspect for State of the Art of Front End","volume":"2010","author":"Usakli","year":"2010","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1109\/TITB.2012.2188536","article-title":"Artifact Removal in Physiological Signals\u2014Practices and Possibilities","volume":"16","author":"Sweeney","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_104","unstructured":"Weston, D. (1991). Electromagnetic Compatability: Principles and Applications, Marcel Dekker."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neunet.2017.02.013","article-title":"Evaluating deep learning architectures for Speech Emotion Recognition","volume":"92","author":"Fayek","year":"2017","journal-title":"Neural Netw."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Huang, Z., Dong, M., Mao, Q., and Zhan, Y. (2014, January 3\u20137). Speech Emotion Recognition Using CNN. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654984"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Lim, W., Jang, D., and Lee, T. (2016, January 13\u201316). Speech Emotion Recognition Using Convolutional and Recurrent Neural Networks. Proceedings of the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea.","DOI":"10.1109\/APSIPA.2016.7820699"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1109\/TMM.2014.2360798","article-title":"Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks","volume":"16","author":"Mao","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Mirsamadi, S., Barsoum, E., and Zhang, C. (2017, January 5\u20139). Automatic speech emotion recognition using recurrent neural networks with local attention. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952552"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.specom.2011.11.004","article-title":"Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition","volume":"54","author":"Sahidullah","year":"2012","journal-title":"Speech Commun."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Mohammadi, M., and Mohammadi, H.R.S. (2017, January 2\u20134). Robust features fusion for text independent speaker verification enhancement in noisy environments. Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran.","DOI":"10.1109\/IranianCEE.2017.7985357"},{"key":"ref_112","first-page":"200","article-title":"Cascaded Subband Energy-Based Emotion Classification","volume":"133","author":"Amarakeerthi","year":"2013","journal-title":"IEEJ Trans. Electron. Inf. Syst."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"363","DOI":"10.25046\/aj030437","article-title":"Amplitude-Frequency Analysis of Emotional Speech Using Transfer Learning and Classification of Spectrogram Images","volume":"3","author":"Lech","year":"2018","journal-title":"Adv. Sci. Technol. Eng. Syst. J."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.ijhcs.2018.12.003","article-title":"Estimating cognitive load from speech gathered in a complex real-life training exercise","volume":"124","author":"Vukovic","year":"2019","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Ko, B.C. (2018). A Brief Review of Facial Emotion Recognition Based on Visual Information. Sensors, 18.","DOI":"10.3390\/s18020401"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/34.598232","article-title":"Coding, analysis, interpretation, and recognition of facial expressions","volume":"19","author":"Essa","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Zhang, L., Tong, Y., and Ji, Q. (2008, January 12\u201318). Active Image Labeling and Its Application to Facial Action Labeling. Proceedings of the European Conference on Computer Vision 2008, Marseille, France.","DOI":"10.1007\/978-3-540-88688-4_52"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/34.908962","article-title":"Recognizing action units for facial expression analysis","volume":"23","author":"Tian","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1080\/20961790.2018.1523703","article-title":"Evaluating OpenFace: An open-source automatic facial comparison algorithm for forensics","volume":"3","author":"Fydanaki","year":"2018","journal-title":"Forensic Sci. Res."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Robinson, P., and Morency, L.-P. (2016, January 7\u20139). OpenFace: An open source facial behavior analysis toolkit. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477553"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"E1454","DOI":"10.1073\/pnas.1322355111","article-title":"Compound facial expressions of emotion","volume":"111","author":"Du","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TFUZZ.2006.889825","article-title":"Fuzzy evaluation of heart rate signals for mental stress assessment","volume":"15","author":"Kumar","year":"2007","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_123","first-page":"39","article-title":"Applying the concept of experton to fuzzy mental workload modeling","volume":"8","author":"Said","year":"2003","journal-title":"Fuzzy Econ. Rev."},{"key":"ref_124","first-page":"B231","article-title":"EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks","volume":"78","author":"Berka","year":"2007","journal-title":"Aviat. Space Environ. Med."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/TITS.2013.2269679","article-title":"Effectiveness of physiological and psychological features to estimate helicopter pilots\u2019 workload: A Bayesian network approach","volume":"14","author":"Besson","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.trc.2013.10.004","article-title":"A hybrid Bayesian Network approach to detect driver cognitive distraction","volume":"38","author":"Liang","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.ijpsycho.2015.10.004","article-title":"Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression","volume":"98","author":"Ke","year":"2015","journal-title":"Int. J. Psychophysiol."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Stansinoupolos, P., Smith, M.H., Hargroves, K., and Desha, C. (2013). Whole System Design: An Integrated Approach to Sustainable Engineering, Routledge.","DOI":"10.4324\/9781849773775"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Yin, Z., Zhang, J., and Wang, R. (2016). Neurophysiological Feature-Based Detection of Mental Workload by Ensemble Support Vector Machines. Advances in Cognitive Neurodynamics, Springer.","DOI":"10.1007\/978-981-10-0207-6_64"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Abraham, A. (2005). Adaptation of fuzzy inference system using neural learning. Fuzzy Systems Engineering, Springer.","DOI":"10.1007\/11339366_3"},{"key":"ref_131","unstructured":"Vieira, J., Dias, F.M., and Mota, A. (2004, January 25\u201327). Neuro-fuzzy systems: A survey. Proceedings of the 5th WSEAS NNA International Conference, Udine, Italy."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ijpsycho.2017.10.004","article-title":"Using theta and alpha band power to assess cognitive workload in multitasking environments","volume":"123","author":"Puma","year":"2018","journal-title":"Int. J. Psychophysiol."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_134","unstructured":"ICAO (2011). Global and Regional 20-Year Forecasts, ICAO."},{"key":"ref_135","unstructured":"Comerford, D., Brandt, S.L., Lachter, J.B., Wu, S.C., Mogford, R.H., Battiste, V., and Johnson, W.W. (2013). NASA\u2019s Single-Pilot Operations Technical Interchange Meeting: Proceedings and Findings."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MAES.2017.160175","article-title":"Commercial airline single-pilot operations: System design and pathways to certification","volume":"32","author":"Lim","year":"2017","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_137","unstructured":"Wolter, C.A., and Gore, B.F. (2015). A Validated Task Analysis of the Single Pilot Operations Concept."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1177\/154193120605000518","article-title":"Assisting Interruption Recovery in Supervisory Control of Multiple Uavs","volume":"50","author":"Scott","year":"2006","journal-title":"Proc. Hum. Factors Ergon. Soc. Annu. Meet."},{"key":"ref_139","unstructured":"Ruff, H.A., Calhoun, G.L., Draper, M.H., Fontejon, J.V., and Guilfoos, B.J. (2004). Exploring Automation Issues in Supervisory Control of Multiple UAVs, Sytronics Inc."},{"key":"ref_140","unstructured":"Lim, Y., Ranasinghe, K., Gardi, A., Ezer, N., and Sabatini, R. (2018, January 9\u201314). Human-machine interfaces and interactions for multi UAS operations. Proceedings of the 31st Congress of the International Council of the Aeronautical Sciences (ICAS 2018), Belo Horizonte, Brazil."},{"key":"ref_141","unstructured":"Lim, Y., Samreeloy, T., Chantaraviwat, C., Ezer, N., Gardi, A., and Sabatini, R. (2019, January 24\u201328). Cognitive Human-Machine Interfaces and Interactions for Multi-UAV Operations. Proceedings of the 18th Australian International Aerospace Congress (AIAC18), Melbourne, Australia."},{"key":"ref_142","unstructured":"Ath\u00e8nes, S., Averty, P., Puechmorel, S., Delahaye, D., and Collet, C. (2002, January 23\u201325). ATC complexity and controller workload: Trying to bridge the gap. Proceedings of the International Conference on Human-Computer Interaction in Aeronautics (HCI-02), Cambridge, MA, USA."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/00140137408931336","article-title":"Sinus Arrhythmia as a Measure of Mental Load","volume":"17","author":"Boyce","year":"1974","journal-title":"Ergonomics"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1080\/00140139.2012.744473","article-title":"Evaluation of head-free eye tracking as an input device for air traffic control","volume":"56","author":"Alonso","year":"2013","journal-title":"Ergonomics"},{"key":"ref_145","unstructured":"Wickens, C., Mavor, A.S., and Mcgee, J.P. (1997). Flight to the Future: Human Factors in Air Traffic Control, National Academies Press."},{"key":"ref_146","unstructured":"Luckowski, S.M. (1975). Bioinstrumentation: Biomedical Results of Apollo."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Cupples, J.S., and Johnson, B.J. (2005). Future Space Bioinstrumentation Systems, SAE Technical Paper.","DOI":"10.4271\/2005-01-2789"},{"key":"ref_148","unstructured":"(2011). National Aeronautics and Space Administration, CHeCS Hardware Catalog."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Kanas, N., and Manzey, D. (2008). Space Psychology and Psychiatry, Springer Science & Business Media.","DOI":"10.1007\/978-1-4020-6770-9"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.ast.2014.02.006","article-title":"Neurocognitive performance using the Windows spaceflight cognitive assessment tool (WinSCAT) in human spaceflight simulations","volume":"35","author":"Gabriel","year":"2014","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TITB.2005.854509","article-title":"A multiparameter wearable physiologic monitoring system for space and terrestrial applications","volume":"9","author":"Mundt","year":"2005","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_152","unstructured":"Board, S.S., and Council, N.R. (2006). A Risk Reduction Strategy for Human Exploration of Space: A Review of NASA\u2019s Bioastronautics Roadmap, National Academies Press."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3465\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:09:40Z","timestamp":1760188180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,8]]},"references-count":152,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19163465"],"URL":"https:\/\/doi.org\/10.3390\/s19163465","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,8]]}}}