Open Access
Issue |
MATEC Web Conf.
Volume 314, 2020
International Cross-Industry Safety Conference (ICSC) – International Symposium on Aircraft Technology, MRO and Operations (ISATECH) (ICSC-ISATECH 2019)
|
|
---|---|---|
Article Number | 01003 | |
Number of page(s) | 14 | |
Section | International Cross-Industry Safety Conference | |
DOI | https://doi.org/10.1051/matecconf/202031401003 | |
Published online | 29 May 2020 |
- Besco, R.O. (1990). Subtle incapacitation of pilots: How to tell if your captain has died. Accident Prevention. [Google Scholar]
- ECA (2012). Pilot fatigue. Available online at https://www.eurocockpit.be/sites/default/files/eca_barometer_on_pilot_fatigue_12_1107_f.pdf [Google Scholar]
- Cropanzano, R. S., Ambrose, M. L., & Van Wagoner, P. (2019). 11 Organizational Justice and Workplace Emotion. Social Psychology and Justice. [Google Scholar]
- Cahill, J., Cullen, P. and Gaynor, K. (2019). Pilot Wellbeing & Work-Related Stress (Wrs). 27th International Symposium on Aviation Psychology. [Google Scholar]
- Gawron, V. (2019). Automation in aviation – Guidelines. Mitre Technical Report MTR190018. McLean, VA: Mitre Corporation. [Google Scholar]
- Levin, E., MR, M., COIMBRA, F., Keller, J. and Teo, A. (2019). Fatigue in Collegiate Aviation.” International Journal of Aviation, Aeronautics, and Aerospace 6.4. [Google Scholar]
- HELLERSTRÖM, D., Eriksson, E., ROMIG, E. and KLEMETS, T. (2010). Flight time limitations and fatigue risk management: comparison of three regulatory approaches”, Boeing Flight Safety Foundation. [Google Scholar]
- Hilditch, C.J., and Flynn-Evans, E.E. (2019). Controlled Rest: Profile of Use, Challenges, and Best Practices. [Google Scholar]
- Rostaminia, S., Mayberry, A., Ganesan, D., Marlin, B. and Gummeson, J. (2017). iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 1(2). pp. 1-26. [CrossRef] [Google Scholar]
- O’Hagan, A.D., Issartel, J., Wall, A., Dunne, F., Boylan, P., Groeneweg, J., Herring, M., Campbell, M. and Warrington, G. (2019). Flying on empty: effects of sleep deprivation on pilot performance. Biological Rhythm Research, pp. 1-22. [CrossRef] [Google Scholar]
- Gateau, T., Ayaz, H. and Dehais, F. (2018). In silico vs. over the clouds: on-the-fly mental state estimation of aircraft pilots, using a functional near infrared spectroscopy-based passive-BCI. Frontiers in human neuroscience 12. [Google Scholar]
- Cordeiro, R. and Carvalho J. (2019) Positive mental health in the workplace: a new global and preventive approach for flight staff. Авіаційна та екстремальна психологія у контексті технологічних. 3. [Google Scholar]
- CAA, NLR, & NIN. (2018). Independent research on pilot fatigue measurement by the Netherlands Aerospace Center. Available at http://publicapps.caa.co.uk/docs/33/Pilot%20Fatigue%20Measurement%20Research_Final%20Report_v0.9.1clean.pdf. [Google Scholar]
- Papanikou, M. (2017). The philosophy of safety: change and the re-organisation of the aviation industry. Ph.D. Thesis, University of Greenwich, UK. [Google Scholar]
- Mohrmann, F., & Stoop, J. (2019). Airmanship 2.0: Innovating aviation human factors forensics to necessarily proactive role. International Society of Aviation Safety Investigators (ISASI). Annual Seminar. [Google Scholar]
- Warr, P. (2001). Age and work behaviour: Physical attributes, cognitive abilities, knowledge, personality traits and motives. International review of industrial and organizational psychology, 16, 1-36. [Google Scholar]
- Moser, D., Anderer, P., Gruber, G., Parapatics, S., Loretz, E., Boeck, M., ... & Saletu, B. (2009). Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters. Sleep, 32(2), 139-149. [CrossRef] [Google Scholar]
- Taylor, J., O’Hara R., Mumenthaler, M. and Yesavage, J. (2000). Relationship of CogScreen-AE to flight simulator performance and pilot age. Aviat Space Environ Med 7. pp. 373-80. [Google Scholar]
- Taylor, J., O’Hara, R., Mumenthaler, M., Rosen, A. & Yesavage, J. (2005). Cognitive Ability, Expertise, and Age Differences in Following Air-Traffic Control Instructions. Psychology and Aging. 20(1). pp. 117-133. [CrossRef] [Google Scholar]
- Corbett, M.A. (2009). Science & Technology Watch: A Drowsiness Detection System for Pilots: Optalert®. Aviation, space, and environmental medicine, 80(2). pp.149-149. [CrossRef] [Google Scholar]
- Frantzidis, C. A., Bratsas, C., Papadelis, C. L., Konstantinidis, E., Pappas, C., & Bamidis, P. D. (2010). Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli. IEEE Transactions on Information Technology in Biomedicine, 14(3), 589-597. [CrossRef] [Google Scholar]
- Spyrou, I. M., Frantzidis, C., Bratsas, C., Antoniou, I., & Bamidis, P. D. (2016). Geriatric depression symptoms coexisting with cognitive decline: a comparison of classification methodologies. Biomedical Signal Processing and Control, 25, 118-129. [CrossRef] [Google Scholar]
- Chriskos, P., Frantzidis, C. A., Gkivogkli, P. T., Bamidis, P. D., & KourtidouPapadeli, C. (2019). Automatic sleep staging employing convolutional neural networks and cortical connectivity images. IEEE transactions on neural networks and learning systems. [Google Scholar]
- Reyes del Paso, G. A., Langewitz, W., Mulder, L. J., Van Roon, A., & Duschek, S. (2013). The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: a review with emphasis on a reanalysis of previous studies. Psychophysiology, 50(5), 477-487. [CrossRef] [Google Scholar]
- Moser, D., Anderer, P., Gruber, G., Parapatics, S., Loretz, E., Boeck, M., ... & Saletu, B. (2009). Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters. Sleep, 32(2), 139-149. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.