Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

[1]  Eling D. de Bruin,et al.  Assessing Saccadic Eye Movements With Head-Mounted Display Virtual Reality Technology , 2020, Frontiers in Psychiatry.

[2]  F. Sauvet,et al.  In-Flight Automatic Detection of Vigilance States Using a Single EEG Channel , 2014, IEEE Transactions on Biomedical Engineering.

[3]  John LaRocco,et al.  A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection , 2020, Frontiers in Neuroinformatics.

[4]  Martin Wolf,et al.  A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology , 2014, NeuroImage.

[5]  D. O’Hare,et al.  Responding to an Unexpected In-Flight Event: Physiological Arousal, Information Processing, and Performance , 2020, Hum. Factors.

[6]  Xiaohua Zhao,et al.  Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study , 2019, International journal of environmental research and public health.

[7]  Anthony D. McDonald,et al.  Steering in a Random Forest , 2014, Hum. Factors.

[8]  J. A. Veltman,et al.  A Comparative Study of Psychophysiological Reactions During Simulator and Real Flight , 2002 .

[9]  Takeshi Tsuchiya,et al.  Biofeedback control of horseback riding simulator , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[10]  Andrés Catena,et al.  Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data. , 2017, Accident; analysis and prevention.

[11]  Kai-Quan Shen,et al.  EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate , 2008, Clinical Neurophysiology.

[12]  Heikki Mansikka,et al.  Fighter pilots' heart rate, heart rate variation and performance during an instrument flight rules proficiency test. , 2016, Applied ergonomics.

[13]  G F Wilson,et al.  The use of cardiac and eye blink measures to determine flight segment in F4 crews. , 1991, Aviation, space, and environmental medicine.

[14]  Shichao Zhang,et al.  Efficient kNN classification algorithm for big data , 2016, Neurocomputing.

[15]  Marina Efthymiou,et al.  An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017. , 2019, Journal of safety research.

[16]  Alex Lloyd,et al.  A comparison of methods used for inducing mental fatigue in performance research: individualised, dual-task and short duration cognitive tests are most effective , 2020, Ergonomics.

[17]  Heng Li,et al.  Identification and classification of construction equipment operators' mental fatigue using wearable eye-tracking technology , 2020, Automation in Construction.

[18]  Rossana Castaldo,et al.  Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis , 2015, Biomed. Signal Process. Control..

[19]  J. Thropp,et al.  PERCLOS as an Indicator of Slow-Onset Hypoxia in Aviation. , 2018, Aerospace medicine and human performance.

[20]  Jinchao Lin,et al.  Considerations in Physiological Metric Selection for Online Detection of Operator State: A Case Study , 2016, HCI.

[21]  Khardi Salah,et al.  Environmental impact reduction of commercial aircraft around airports. Less noise and less fuel consumption , 2013, European Transport Research Review.

[22]  Susana Rodrigues,et al.  Wearable Biomonitoring Platform for the Assessment of Stress and its Impact on Cognitive Performance of Firefighters: An Experimental Study , 2018, Clinical practice and epidemiology in mental health : CP & EMH.

[23]  Yu Zhang,et al.  Evaluation of Strategies for Integrated Classification of Visual-Manual and Cognitive Distractions in Driving , 2016, Hum. Factors.

[24]  Micheal Drieberg,et al.  A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability , 2017, Sensors.

[25]  Lauren Reinerman-Jones,et al.  Workload Is Multidimensional, Not Unitary: What Now? , 2015, HCI.

[26]  Andrea Alaimo,et al.  Aircraft Pilots Workload Analysis: Heart Rate Variability Objective Measures and NASA-Task Load Index Subjective Evaluation , 2020, Aerospace.

[27]  N. Wright,et al.  Vigilance on the civil flight deck: incidence of sleepiness and sleep during long-haul flights and associated changes in physiological parameters , 2001, Ergonomics.

[28]  Weiqiang Zhang,et al.  Detection of mental fatigue state with wearable ECG devices , 2018, Int. J. Medical Informatics.

[29]  D. Schroeder,et al.  Blink Rate: A Possible Measure of Fatigue , 1994, Human factors.

[30]  J. C. Miller,et al.  Electrooculographic and performance indices of fatigue during simulated flight , 1996, Biological Psychology.

[31]  Leandro L. Di Stasi,et al.  Eye Movements Research in Aviation: Past, Present, and Future , 2019, Improving Aviation Performance through Applying Engineering Psychology.

[32]  W. Bardwell,et al.  Effects of stress on heart rate complexity—A comparison between short-term and chronic stress , 2009, Biological Psychology.

[33]  Hankins Tc,et al.  A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight. , 1998, Aviation, space, and environmental medicine.

[34]  Min Zhao,et al.  Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic , 2011, Expert Syst. Appl..

[35]  Kouhyar Tavakolian,et al.  Blending Human and Machine: Feasibility of Measuring Fatigue Through the Aviation Headset , 2020, Hum. Factors.

[36]  Kai Xie,et al.  Intelligent Recognition of Fatigue and Sleepiness Based on InceptionV3-LSTM via Multi-Feature Fusion , 2020, IEEE Access.

[37]  Lorenzo Sabattini,et al.  Wearable Devices for the Assessment of Cognitive Effort for Human–Robot Interaction , 2020, IEEE Sensors Journal.

[38]  Jinxing Lin,et al.  Pilots’ Fatigue Status Recognition Using Deep Contractive Autoencoder Network , 2019, IEEE Transactions on Instrumentation and Measurement.

[39]  Christopher A.B.Arockia,et al.  Large-scale data analysis on aviation accident database using different data mining techniques , 2016, The Aeronautical Journal.

[40]  Kouhyar Tavakolian,et al.  Spectral Analysis of EEG During Microsleep Events Annotated via Driver Monitoring System to Characterize Drowsiness , 2020, IEEE Transactions on Aerospace and Electronic Systems.

[41]  Janko Drnovsek,et al.  Non-contact heart rate and heart rate variability measurements: A review , 2014, Biomed. Signal Process. Control..

[42]  Qiufeng Wu,et al.  Discovery and Prediction of Stock Index Pattern via Three-Stage Architecture of TICC, TPA-LSTM and Multivariate LSTM-FCNs , 2020, IEEE Access.

[43]  J. Caldwell,et al.  Alertness management strategies for operational contexts. , 2008, Sleep Medicine Reviews.

[44]  T. Balkin,et al.  Fatigue models for applied research in warfighting. , 2004, Aviation, space, and environmental medicine.

[45]  Ping Wang,et al.  Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system , 2017, PloS one.

[46]  Pawel Strumillo,et al.  Eye-blink detection system for human–computer interaction , 2011, Universal Access in the Information Society.

[47]  Peter R Murphy,et al.  Pupil Diameter Tracks Lapses of Attention , 2016, PloS one.

[48]  Chong Zhang,et al.  Automatic recognition of cognitive fatigue from physiological indices by using wavelet packet transform and kernel learning algorithms , 2009, Expert Syst. Appl..

[49]  Hikmat Ullah Khan,et al.  A Survey on State-of-the-Art Drowsiness Detection Techniques , 2019, IEEE Access.

[50]  Ilias Tachtsidis,et al.  A Review on the Use of Wearable Functional Near-Infrared Spectroscopy in Naturalistic Environments. , 2018, The Japanese psychological research.

[51]  Michiel A. J. Kompier,et al.  The window of my eyes: Task disengagement and mental fatigue covary with pupil dynamics , 2015, Biological Psychology.

[52]  Chongxun Zheng,et al.  Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. , 2012, Accident; analysis and prevention.

[53]  Stephen P. Boyd,et al.  Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data , 2017, KDD.

[54]  Michael B. McCamy,et al.  Effects of long and short simulated flights on the saccadic eye movement velocity of aviators , 2016, Physiology & Behavior.

[55]  David F Neri,et al.  Fatigue countermeasures in aviation. , 2009, Aviation, space, and environmental medicine.

[56]  Jianfeng Hu,et al.  Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model , 2018, Cognitive Neurodynamics.

[57]  Maarten A. S. Boksem,et al.  Mental fatigue: Costs and benefits , 2008, Brain Research Reviews.

[58]  Ziho Kang,et al.  Analyzing pilots’ fatigue for prolonged flight missions: Multimodal analysis approach using vigilance test and eye tracking , 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

[59]  Daniel W. Repperger,et al.  Evaluation of Eye Metrics as a Detector of Fatigue , 2011 .

[60]  Z. Vokac,et al.  Phase-shifts of apparent circadian rhythms due to west and east transmeridian flights or to corresponding night-shift sleep displacements. , 1984, Chronobiology international.

[61]  K. Trimmel.  Sensitivity of HRV parameters including pNNxx proven by short-term exposure to 2700 m altitude , 2011, Physiological measurement.

[62]  Frédéric Dehais,et al.  Detecting Pilot's Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario , 2018, Front. Hum. Neurosci..

[63]  Peter Rossiter,et al.  Applying neural network analysis on heart rate variability data to assess driver fatigue , 2011, Expert Syst. Appl..

[64]  J. L. Greig,et al.  Involuntary eye responses as measures of fatigue in US Army Apache aviators. , 2005, Aviation, space, and environmental medicine.

[65]  José J. Cañas,et al.  Saccadic peak velocity as an alternative index of operator attention: A short review , 2013 .

[66]  Ivan Ho Mien,et al.  Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation. , 2012, Sleep.

[67]  Andrés Catena,et al.  Fatigue in the military: towards a fatigue detection test based on the saccadic velocity , 2016, Physiological measurement.

[68]  Seong-Whan Lee,et al.  Classification of pilots’ mental states using a multimodal deep learning network , 2020 .

[69]  Glenn F. Wilson,et al.  Performance and Psychophysiological Measures of Fatigue Effects on Aviation Related Tasks of Varying Difficulty , 2007 .

[70]  David C Christiani,et al.  Heart Rate Variability and Performance of Commercial Airline Pilots during Flight Simulations , 2019, International journal of environmental research and public health.

[71]  Craig S Schallhorn,et al.  Vigilance Aid Use and Aircraft Carrier Landing Performance in Pilots of Tactical Aircraft. , 2020, Aerospace medicine and human performance.

[72]  Jin Ma,et al.  Posturographic Balance's Validity in Mental and Physical Fatigue Assessment Among Cadet Pilots. , 2018, Aerospace Medicine and Human Performance.

[73]  The relationship between workload, performance and fatigue in a short-haul airline , 2020, Chronobiology international.

[74]  E. Olivier,et al.  Dissociation between mental fatigue and motivational state during prolonged mental activity , 2015, Front. Behav. Neurosci..

[75]  Michael G. Lenné,et al.  The relative importance of real-time in-cab and external feedback in managing fatigue in real-world commercial transport operations , 2017, Traffic injury prevention.

[76]  Tzu-Tsung Wong,et al.  Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation , 2015, Pattern Recognit..

[77]  Jean-Claude Martin,et al.  Joint Attention Simulation Using Eye-Tracking and Virtual Humans , 2014, IEEE Transactions on Affective Computing.

[78]  Li Yang,et al.  On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice , 2020, Neurocomputing.

[79]  Zhihong Wen,et al.  Developing a fatigue questionnaire for Chinese civil aviation pilots , 2018, International journal of occupational safety and ergonomics : JOSE.

[80]  Vsevolod Peysakhovich,et al.  Assessment of Ocular and Physiological Metrics to Discriminate Flight Phases in Real Light Aircraft , 2018, Hum. Factors.

[81]  Wilson S. Geisler,et al.  Real-time simulation of arbitrary visual fields , 2002, ETRA.

[82]  N. Christie,et al.  Sleepiness on the flight deck: Reported rates of occurrence and predicted fatigue risk exposure associated with UK airline pilot work schedules , 2020 .

[83]  Hua Ling Deng,et al.  Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering , 2019, Int. J. Agric. Environ. Inf. Syst..

[84]  Yi Ouyang,et al.  Estimating VDT Mental Fatigue Using Multichannel Linear Descriptors and KPCA-HMM , 2008, EURASIP J. Adv. Signal Process..

[85]  J. Stern,et al.  The endogenous eyeblink. , 1984, Psychophysiology.

[86]  João Paulo Silva Cunha,et al.  A wearable approach for intraoperative physiological stress monitoring of multiple cooperative surgeons , 2019, Int. J. Medical Informatics.

[87]  D. Bai,et al.  Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature , 2018, Psychiatry investigation.

[88]  Anna Anund,et al.  Comparison of outlier heartbeat identification and spectral transformation strategies for deriving heart rate variability indices for drivers at different stages of sleepiness , 2018, Traffic injury prevention.

[89]  Caio Bezerra Souto Maior,et al.  Real-time classification for autonomous drowsiness detection using eye aspect ratio , 2020, Expert Syst. Appl..

[90]  Chih-Jen Lin,et al.  A study on reduced support vector machines , 2003, IEEE Trans. Neural Networks.

[91]  Zhiwei Zhu,et al.  Robust real-time eye detection and tracking under variable lighting conditions and various face orientations , 2005, Comput. Vis. Image Underst..

[92]  V. Vuksanović,et al.  Heart rate variability in mental stress aloud. , 2007, Medical engineering & physics.

[93]  Helena Canhão,et al.  Prevalence of fatigue in a group of airline pilots. , 2013, Aviation, space, and environmental medicine.

[94]  Alicja Bortkiewicz,et al.  Application of eye-tracking in the testing of drivers: A review of research. , 2015, International journal of occupational medicine and environmental health.

[95]  Fred H. Previc,et al.  The Effects of Sleep Deprivation on Flight Performance, Instrument Scanning, and Physiological Arousal in Pilots , 2009 .

[96]  Fan Wang,et al.  Estimating Driving Fatigue at a Plateau Area with Frequent and Rapid Altitude Change , 2019, Sensors.

[97]  Valerie Gawron,et al.  Summary of Fatigue Research for Civilian and Military Pilots , 2016 .

[98]  Thibault Gateau,et al.  In silico vs. Over the Clouds: On-the-Fly Mental State Estimation of Aircraft Pilots, Using a Functional Near Infrared Spectroscopy Based Passive-BCI , 2018, Front. Hum. Neurosci..