Experimental Study of the Correlation Between the Level of Professional Training and the Dynamics of Changes in the Psycho-emotional and Functional State of a Person During Testing

Abstract

The urgency of the objective assessment of the professional training level of hazardous objects workers is shown. The ability to act quickly and correctly in abnormal and emergency situations is highlighted as one of the important competences. An experimental study of the correlation between the level of professional training and the dynamics of changes in the functional and psycho-emotional state during testing is carried out. Remote and non-contact technologies is used to monitor the current functional and psycho-emotional state. Such technologies allow in completely passive mode to register the main bio-parameters of the test person. To visualize the current state, it is suggested to use pie charts. The carried out experimental researches have allowed to allocate the most informative factors, correlating with a level of professional training. Such factors, first of all are: the final level of mental and physical fatigue of the tested; the total amount of physical and mental forces spent on the test; the level of manifestation of the state of fright, fear and confusion when acquainted with the test task. The comprehensive consideration of the aforementioned factors will increase the objectivity of assessing the level of professional training.


 


 


Keywords: human factor, bio-parameters registration, psycho-emotional state

References
[1] Alyushin, M. V., Kolobashkina, L. V., and Khazov, A. V. (2015). Professional’nyj otbor personala po psihologicheskim kachestvam na osnove metodov, razrabotannyh v ramkah teorii prinyatiya reshenij [Selection of professional staff according to psychological characteristics with the help of methods developed on the basis of the decision-taking theory]. Voprosy Psikhologii, vol. 2, pp. 88–94.


[2] Alyushin, M. V., Alyushin, A. V., Andryushina, L. O. et al. (2013). Distancionnye i nekontaktnye tekhnologii registracii bioparametrov operativnogo personala kak sredstvo upravleniya chelovecheskim faktorom i povysheniya bezopasnosti AEHS [Distant and noncontact technologies for registration of operating personnel bio parameters as a mean of human factor control and NPP security improvement]. Global Nuclear Safety, vol. 3, no. 8, pp. 69–77.


[3] Alyushin, M. V. and Kolobashkina, L. V. (2014). Monitoring bioparametrov cheloveka na osnove distantsionnyih tehnologiy [Monitoring human biometric parameters on the basis of distance technologies]. Voprosy Psikhologii, vol. 6, pp. 135–144.


[4] Alyushin, M. V., Alyushin, V. M., Dvoryankin, S. V., et al. (2013). Akusticheskie tekhnologii dlya intellektual’nyh sistem monitoringa funkcional’nogo sostoyaniya operativnogo sostava upravleniya ob”ektami atomnoj ehnergetiki [Acoustic technologies for ‘intellectual’ monitoring systems of atomic energetic objects’ operational control staff current functional state]. Global Nuclear Safety. vol. 4, no. 9, pp. 63–71.


[5] Alyushin, V. M. (2015). Diagnostika psihoemocional’nogo sostoyaniya na osnove sovremennyh akusticheskih tekhnologij [Diagnostics of emotional states on the basis of contemporary acoustic technologies]. Voprosy Psikhologii, vol. 3, pp.145– 152.


[6] Alyushin, V. M. (2016). Spektral’nyj analiz rechevoj deyatel’nosti kak sposob ocenki psihologicheskogo klimata v kollektive [Spectral analysis of speech as a means of assessing psychological team climate]. Voprosy Psikhologii, vol. 3, pp. 148–156.


[7] Alyushin, M. V., Alyushin, А. V., Belopolsky, V. M., et al. (2013). Opticheskie tekhnologii dlya sistem monitoringa tekushchego funkcional’nogo sostoyaniya operativnogo sostava upravleniya ob”ektami atomnoj ehnergetiki [Optical technologies for the operational staff current functional state monitoring systems for the atomic energy objects]. Global Nuclear Safety, vol. 2, no. 7, pp. 69–77.


[8] Rahardja, A., Sowmya, A., and Wilson, W. (1991). A neural network approach to component versus holistic recognition of facial expressions in images. Intelligent Robots and Computer Vision X: Algorithms and Techniques, vol. 1607, pp. 62–70.


[9] Adolphs, R., Tranel, D., Damasio, H., et al. (1994). Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala. Letters to Nature, vol. 372, pp. 669–672.


[10] Gunes, H. and Piccardi, M. (2009). Automatic temporal segment detection and affect recognition from face and body display. IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, no. 1, pp. 64–84.


[11] Valstar, M. F., Mehu, M., Jiang, B., et al. (2012). Metaanalysis of the first facial expression recognition challenge. IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 42, no. 4, pp. 966–979.


[12] Ma, L. and Khorasani, K. (2004). Facial expression recognition using constructive feedforward neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 34, no. 3, pp. 1588–1595.


[13] Grigorescu, C., Petkov, N., and Westenberg, M. A. (2003). Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing, vol. 12, no. 7, pp. 729–739.


[14] Shan, C., Gong, S., and McOwan, P. W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, vol. 27, no. 6, pp. 803–816.


[15] Fontaine, R. J., Scherer, K. R., Roesch, E. B., et al. (2007). The world of emotions is not two-dimensional. Psychological Science, vol. 18, no. 12, pp. 1050–1057.


[16] Essa, I. and Pentland, A. ( July 1997). Coding, analysis, interpretation, and recognition of facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 757–763.


[17] US Department of Transportation. Federal Motor Carrier Safety Administration. (2009). An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies: Final Report, pp. 1–41.


[18] Ji, Q., Zhu, Z., and Lan, P. (2004). Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technology, vol. 53, no. 4, pp. 1052– 1068.


[19] Recarte, M. A. and Nunes, L. M. (2003). Mental workload while driving: Effects on visual search, discrimination and decision making. Journal of Experimental Psychology: Applied, vol. 9, no. 2, pp. 119–137.


[20] Corbetta, M. and Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, vol. 3, pp. 201–215.


[21] Viatkin, В. А. (1979). Spektral’nyj analiz golosa kak bezkontaktnyj metod issledovaniya psihicheskogo stressa v sporte [Spectral voice analysis as a contactless method for studying mental stress in sports]. Aktual’nye voprosy teorii i praktiki fizicheskogo vospitaniya i sporta. Perm’, pp. 8–9.


[22] Viatkin, В. А. and Markelov, V. V. (2010). Permskie simpoziumy «Psihicheskij stress v sporte» [Perm symposia ‘Mental stress in sport’]. Sportivnyj psiholog, vol. 1, no. 19, pp. 91–96.


[23] Popova, V. V. (2011). Stress i sovladanie v sporte v svete teorii integral’noj individual’nosti [Stress and coping in sport in terms of the theory of integral individuality]. Theory and Practice of Social Development, vol. 8, pp. 143–146.


[24] Jarlier, S., Grandjean, D., Delplanque, S., et al. (2011). Thermal analysis of facial 8.


[25] Abramova, V. N., Alyushin, M. V., and Kolobashkina, L. V. (2014). Psihologicheskij trening stressoustojchivosti na osnove distancionnyh nekontaktnyh tekhnologij registracii bioparametrov [Psychological training of resistance to stress on the basis of distance no-contact technologies of registering biological parameters ]. Voprosy Psikhologii, vol. 6, pp. 144–152.