CRITERIA FOR ESTIMATING THE SENSORIMOTOR REACTION TIME BY THE SMALL UAV OPERATOR

Authors

  • T. A. Vakaliuk Zhytomyr Polytechnic State University, Zhytomyr, Ukraine., Ukraine
  • I. A. Pilkevych Zhytomyr military institute named after S. P. Korolov, Zhytomyr, Ukraine., Ukraine
  • A. M. Tokar Zhytomyr military institute named after S. P. Korolov, Zhytomyr, Ukraine., Ukraine
  • R. I. Loboda Zhytomyr military institute named after S. P. Korolov, Zhytomyr, Ukraine., Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2021-2-19

Keywords:

evaluation criteria, small UAV operator, sensorimotor reaction time, distribution density.

Abstract

Context. The rapid development of science and technology predetermines a significant expansion of the fields of application of UAVs different purposes. The key to the effective use UAVs is high-quality training of operators, an important element of which is the PPS of candidates, in particular, the assessment of their sensorimotor reactions. This can be achieved by selecting and justifying appropriate criteria.

Objective. The goal of the work is the justification criteria for estimating the time sensorimotor reactions of a small UAV operator by analyzing the density distribution of statistical data.

Method. A method has been developed to determine criteria for evaluating the time of sensorimotor reactions a small UAV operator based on the accumulation statistical material and its mathematical processing based on the results of a field experiment. The method allows to estimate numerical characteristics the distribution of the average reaction time in three modes: training production, in the conditions overload, in the conditions of overtraining and to obtain a generalized estimation. It was possible, by analyzing the occasional noninterruptible values, which take values within a certain range of values, to establish standards against which the obtained values the sensorimotor reaction time of the small UAV operator are compared and a decision is made on their suitability for training.

Results. We obtained statistical series for the modes of assessment: skill development, under obstacle conditions, under conditions skill restructuring. For a visual representation of the series the corresponding histograms the distribution of the average reaction time duration were constructed. In order to eliminate the representativeness error, statistical series alignment was carried out by selecting a theoretical distribution curve for each series, which displays only essential features of the statistical material. For this purpose, we approximated the histogram of distribution by the polynomialf fourth degree. The interval theoretical density of distribution, in which the time sensomotor reaction of an arbitrary person is considered normal, with a given probability reliability such event – 0.95 has been established. To verify the effectiveness of the proposed method, algorithms for estimating the sensorimotor reaction time of a small UAV operator in three modes have been synthesized and the corresponding software that implements the proposed algorithms has been developed.

Conclusions. The criteria for evaluating the sensorimotor reaction time for UAV operator to a visual stimulus using specialized software were substantiated. This allowed the previous PPS training candidates to take into account the requirements to the motor skills of the small UAV operator and the specificity his movements. The conducted experiments confirmed the validity of decisions made. Prospects for further research may include expansion of testing modes with justification for appropriate evaluation criteria.

Author Biographies

T. A. Vakaliuk, Zhytomyr Polytechnic State University, Zhytomyr, Ukraine.

Dr. Sc., Professor, Professor at the Department of software engineering.

I. A. Pilkevych, Zhytomyr military institute named after S. P. Korolov, Zhytomyr, Ukraine.

Dr. Sc., Professor, Professor at the Department of computer information technologies.

A. M. Tokar, Zhytomyr military institute named after S. P. Korolov, Zhytomyr, Ukraine.

PhD, Head of the Division of science center.

R. I. Loboda, Zhytomyr military institute named after S. P. Korolov, Zhytomyr, Ukraine.

Research Officer of the Division of science center.

References

Williams K.W. Human Factors Implications of Unmanned Aircraft Accidents: Flight-Control Problems, Human Factors of Remotely Operated Vehicles (Advances in Human Performance and Cognitive Engineering Research), 2006, Vol. 7, pp. 105– 116. DOI: 10.1016/S1479-3601(05)07008-6

Wiegmann D., Shappell S. A human error approach to aviation accident analysis: the Human Factors Analysis and Classification System. Burlington, VT, Ashgate, 2003, 165 p. DOI: 10.4324/9781315263878-3

Goodrich M. A., Cummings M. L. Human Factors Perspective on Next Generation Unmanned Aerial Systems, Handbook of Unmanned Aerial Vehicles, Springer-Verlag, 2015, pp. 2405– 2423. DOI: 10.1007/978-90-481-9707-1_23

Oncu M., Yildiz S. An analysis of human causal factors in unmanned aerial vehicle (UAV) accidents. alifornia, Monterey, December 2014, 91 p. DOI: 10.21236/ada620843

Qi S., Wang F., Jing L. Aircraft System Pilot/Operator Qualification Requirements and Training Study, MATEC Web Conference: Second International Conference on Mechanical, Material and Aerospace Engineering, 2MAE, 2018, Vol. 179, 03006. DOI: 10.1051/matecconf/201817903006

Havlikova M., Jirglb M., Bradac Z. Human Reliability in ManMachine Systems, Procedia Engineering, 2015, Vol. 100, pp. 1207–1214. DOI: 10.1016/j.proeng.2015.01.485

Chung W. K. Reliability evaluation of a human operator under various levels of stress, Microelectronics Reliability, 1991, Vol. 31, № 6, pp. 1251–1255. DOI: 10.1016/0026-2714(91)90315-X

Petrov A., Volodina K., Belyaevam T. The role of the psychophysiological characteristics of a person in his professional development, Education and Self Development, 2019. Vol. 14, No. 4, pp. 63–71. DOI: 10.26907/esd14.4.06

Hryshchenko Y. Reliability problem of ergatic control systems in aviation, Methods and Systems of Navigation and Motion Control (MSNMC), Fourth International Conference. Kiev, 18– 20 October 2016, pp. 126–129. DOI: 10.1109/msnmc.2016.7783123

Lancaster R., Baseman E. Smolinski L. A quality improvement project: Defining and operationalizing a holistic admission selection policy, Journal of Professional Nursing, 2020, Vol. 36, No. 4, pp. 259–263. DOI: 10.1016/j.profnurs.2020.01.003

Melcher W., Neumann M., Eißfeldt H., Schwab A. Cognitive and psychomotor requirements for operators of military UAS, 61st Conference of the International Military Testing Association in Tallinn, 7–11 October 2019, 11 p.

Ermakov A. Simple and Complex Sensomotor Reaction for Choice when Teaching Protection Against Armed Attacker, First International Volga Region Conference on Economics, Humanities and Sports (FICEHS 2019), 18 January 2020, pp. 772–774. DOI: 10.2991/aebmr.k.200114.185

Tadema J., Theunissen E. A concept for UAV operator involvement in airborne conflict detection and resolution, 27th Digital Avionics Systems Conference, St. Paul, MN, USA, 26–30 October 2008, pp. 4.C.1–1–4.C.1–12. DOI: 10.1109/DASC.2008.4702829

Chappelle W., Tran N., Thompson W., Goodman T., Hyde K. Intelligence and neuropsychological aptitude testing of U.S. Air Force MQ-1 Predator pilot training candidates, Wright-Patterson AFB (OH): U.S. Air Force School of Aerospace Medicine, 2012. Technical Report AFRL-SA-WP-TR-2013-0003. DOI: 10.21236/ada577826

Chappelle W., Heerema B., Thompson W. Factor analysis of computer-based Multidimensional Aptitude Battery-Second Edition intelligence testing from rated U. S. Air Force pilots. Wright-Patterson AFB (OH): U.S. Air Force School of Aerospace Medicine, 2012, Technical Report AFRL-SA-WP-TR-2013-0005. DOI: 10.21236/ada583710

Chappelle W., Goodman T., Swearingen J., Thompson W. A Preliminary Investigation into Cognitive Aptitudes Predictive of Overall MQ-1 Predator Pilot Qualification Training Performance, Wright-Patterson AFB (OH): U.S. Air Force School of Aerospace Medicine, 2015. Technical Report AFRL-SA-WP-SR-2015-0025, 14 p.

Ryan W., Matthews G., Lin J., Szalma J., Calhoun G., Funke G., Chiu C-Y., Ruff H. Vigilance and Automation Dependence in Operation of Multiple Unmanned Aerial Systems (UAS): A Simulation Study, Hum Factors, May 2019, Vol. 61, No. 3, pp. 488–505. DOI: 10.1177/0018720818799468

Kukushkin Ju., Ajvazjan S. Metodika avtomatizirovannoj obrabotki upravljajushhih dvizhenij operatora v prikladnyh issledovanijah nadezhnosti jergaticheskih sistem, Kibernetika i programmirovanie, 2018, No. 5, pp. 15–23. DOI: 10.25136/2306-4196.2018.5.1817

Gusev D., Klimov R. Programmnyj kompleks apriornogo ocenivanija pokazatelej kachestva professional’noj dejatel’nosti operatora jergaticheskoj sistemy, Programmnye sistemy i vychislitel’nye metody, 2015, No. 4. pp. 374–389. DOI: 10.7256/2305-6061.2015.4.17965

Ignatova Y. Makarova I., Yakovleva K., Aksenova A. Visualmotor reactions as an indicator of CNS functional state, Ulyanovsk Medico-biological Journal, 2019, No. 3, pp. 38–51. DOI: 10.34014/2227-1848-2019-3-38-51

Werner L. Probability Theory. De Gruyter Textbook, 2016, 395 p. DOI: 10.1515/9783110466195

Kulakov A. Osobennosti prostoj psihofiziologicheskoj reakcii, Fiziologija cheloveka, 2018, Tom 44, No. 4, pp. 60–66. DOI: 10.1134/s0131164618040069

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Published

2021-07-10

How to Cite

Vakaliuk, T. A., Pilkevych, I. A., Tokar, A. M., & Loboda, R. I. (2021). CRITERIA FOR ESTIMATING THE SENSORIMOTOR REACTION TIME BY THE SMALL UAV OPERATOR . Radio Electronics, Computer Science, Control, (2), 189–197. https://doi.org/10.15588/1607-3274-2021-2-19

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Section

Control in technical systems