FORMALIZATION OF A SET OF INFORMATIVE SIGNS THE DYNAMICS OF MANIPULATION BY CONTROL DEVICES TO SOLVING THE PROBLEM OF DIAGNOSING THE PRODUCTIVITY OF BTS OPERATORS
Abstract
Informative signs of the dynamics of manipulation by control devices such as a mouse and keyboard play an important role in the development of software complexes for the identification of biotechnical systems (BTS) operators by their individual dynamics, in solving problems of diagnostics of various psycho emotional states and operator productivity. It finds application in the spheres of technical and law enforcement security, medical and energy spheres, etc. The purpose of this work is to formalize a set of informative signs of the dynamics of manipulation by control devices to solving the problem of diagnosing the productivity of BTS operators. To achieve this goal, an overview of the most frequently used features is presented, the Bayesian approach is considered in the statistical formulation of the recognition problem, a set of informative sings of the dynamics of keyboard handwriting and the dynamics of mouse manipulations is formalized basedon the results of a number of works. Operator productivity forecast based on fuzzy rules based on selected criteria gave an accuracy of more than 90%. The advantage of using the dynamics of manipulation of the control devices of the BTS operators is the absence of special equipment that requires additional costs.
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DSPA-2019, Moscow, March 27-29, 2019], pp. 525-529.
3. Skrinnikova A.V. Izmenenie individual'noy dinamiki manipulyatsiy ustroystvami upravleniya
kursorom pod vliyaniem emotsiy strakha i radosti [Changing the individual dynamics of manipulations
of cursor control devices under the influence of emotions of fear and joy], Izvestiya YuFU.
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4. Bobyr' M.V., Skrinnikova A.V., Milostnaya N.A., Seregin S.P. Nechetkaya biotekhnicheskaya
sistema upravleniya proizvoditel'nost'yu cheloveka-operatora [Bioengineering fuzzy control
system performance of the human operator], Meditsinskaya tekhnika [Medical equipment],
2017, No. 4 (304), pp. 46-50.
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Swiss federal institute of technology. Zurich, 2008, 112 p.
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deyatel'nosti cheloveka-operatora v kvazistaticheskoy funktsional'noy srede [Finite Markov
chains in the model representation of human operator activity in a quasi-static functional environment],
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[Scientific and Technical Bulletin of Information Technologies, Mechanics and Optics], 2016,
Vol. 16, No. 3, pp. 524-532.
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data, A. Barletta(DE), B. Moser(DE), M. Mayer(DE); assignee Sony Duetschland GmbH,
Cologne (DE), 13.04.2010, 10 p.
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dvizhushchimisya ob"ektami [Control systems for moving objects], Izvestiya RAN. Teoriya i
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9. Shlezinger M.I., Glavach V. Desyat' lektsiy po statisticheskomu i strukturnomu
raspoznavaniyu [Ten lectures on statistical and structural recognition]. Kiev: Naukova Dumka,
2004, 546 p.
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monografiya [Mathematical methods for evaluating the usefulness of diagnostic signs: monograph].
Kiev: Osvita Ukrainy, 2010, 152 p.
11. Dowland P., Furnell S. A long-term trial of keystroke profiling using digraph, trigraph, and
keyword latencies, Proc. of IFIP/SEC – 19th International Conf. on Information Security, Toulouse,
France, 2004, pp. 275-289.
12. Weiss A., Ramapanicker A., Shah P. [at al]. Mouse Movements Biometric Identification:
A Feasibility Study, Proc. of CSIS, Pace Univ., May 2007, pp. 21-28.
13. Singh S., Dr. K.V. Arya. Mouse interaction based authentication system by classifying the distance
travelled by the mouse, International Journal of Computer Applications, March 2011,
Vol. 17, No. 1. Available at: http://www.ijcaon-line.org/volume17/number1/pxc3872752.pdf
(accessed 29 November 2011).
14. Kaklauskas A., Zavadskas E.K., Seniut M.[at al]. Web-based biometric mouse decision
support system for user’s emotional and labour productivity analysis, The 25th Int. Symp.
Automation and Robotics in Construction, 2008, pp. 69-75.
15. Maehr W. Estimation of the user’s emotional state by mouse motions: diploma thesis for
Fachhochschule Vorarlberg ; iTec – Information and Communication Engineering. Dornbirn,
Austria, August 2005, pp. 145.
16. Nazar A., Traore I., Ahmed A.A.E. Inverse biometrics for mouse dynamics, Int. Journ. of Pattern
Recognition and Artificial Intelligence, 2008, Vol. 22, No. 3, pp. 461-495.
17. Akhremchik O.L., Bazulev I.I. Programmnyy kompleks dlya izmereniya vremeni audiomotornykh
reaktsiy operatorov sistem upravleniya khimiko-tekhnologicheskimi protsessami
[Software package for measuring the time of audio-motor reactions of operators of control systems
for chemical and technological processes], Programmnye produkty i sistemy [Software
products and systems], 2017, No. 2 (30), pp. 328-332.
18. Tsagarelli Yu.A. Trudy E.P. Il'ina kak entsiklopediya sovremennoy psikhologii [Ilyin is like an
encyclopedia of modern psychology], Psikhologiya cheloveka v obrazovanii [Human psychology
in education], 2019, Vol. 1, No. 4, pp. 330-340. DOI: 10.33910/2686-9527-2019-1-4-330-340.
19. Hughes M., Aulck L., Johnson P.W. Are there differences in typing performance and typing
forces between short and long travel keyboards, Reviews of Human Factors and Ergonomics,
Sep. 2011, Vol. 55, pp. 954-957.
20. Giot R., El-Abed M., Rosenberger Ch. Keystroke dynamics authentication, Biometrics. Pub:
InTech, 2011, pp. 157-182.
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Published:
2021-01-19
Issue:
Section:
SECTION III. INFORMATION ANALYSIS
