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Assessing the Aircraft Crew Activity Basing on Video Oculography Data 144
Kuravsky L.S.
Doctor of Engineering, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-3375-8446
e-mail: l.s.kuravsky@gmail.com Yuryev G.A.
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-2960-6562
e-mail: g.a.yuryev@gmail.com Zlatomrezhev V.I.
Head of Laboratory, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia
ORCID: https://orcid.org/0000-0003-1776-6881
e-mail: vizlatomr@2100.gosniias.ru Greshnikov I.I.
Lead Engineer, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia
ORCID: https://orcid.org/0000-0001-5474-3094 Polyakov B.Yu.
Graduate Student, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-6457-9520
e-mail: deslion@yandex.ru
Mathematical models and methods for crew training level assessing based on video oculography data are presented. The results obtained are based on comparing the studied fragments of oculomotor activity of pilots with comparable patterns of video oculography data of various types and performance quality contained in a pre-formed specialized database. To obtain estimates, a complex combination of random process analysis and multivariate statistical analysis is used. The “intelligence” of diagnostic tools is contained in empirical data and can flexibly change as they accumulate. The considered example of determining the flight mode and pilot qualification based on video oculography data allows us to talk about the possibility of significant discrimination of the gaze movement trajectories of pilots at different flight phases and significant discrimination of the gaze movement trajectories of experienced and inexperienced pilots at certain phases of flight. An important new component of the presented results is a discriminant analysis for solving the problem of flight exercises classification, based on the principles of quantum computing. The scope of the considered approach is not limited to aviation applications and can be extended to tasks that are similar in content.
Keywords: crew training level assessing, video oculography, Discriminant Analysis, Multidimensional Scaling, Cluster Analysis, oculomotor activity indexes.
Column: Labor Psychology
DOI: https://doi.org/10.17759/exppsy.2021140110
Funding. This work was performed as part of the “SAFEMODE” project (grant # 814961) with the financial support of the Ministry of Science and Higher Education of the Russian Federation (UID RFMEFI62819X0014 project)
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