Bernstein’s Theory of Levels and Its Application for Assessing the Human Operator State

  • Sergey SuyatinovEmail author
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)


Currently, the essence of intelligence and, accordingly, the mechanisms of its implementation are represented in two ways. In the first case, it is based on speculative conclusions, dressed in one or another mathematical form. In the second case, it is based on a biological model of intelligence, formed in living systems in the process of evolution and adaptation to changing external influences. The nervous system of living organisms is the biological embodiment of intelligence. The greatest perfection of intelligence shows in the organization of motion control. The article deals with the origins and basic provisions of the biological theory of levels of human movement regulation. This theory was proposed by the Russian scientist Bernstein, one of the founders of biomechanics. It is shown how the new neural structures (layers) appeared in the process of evolution and complication of the movements of living organisms. These structures, receiving information from sensory fields, formed the corresponding “semantic” behavior models and control commands for their implementation. Features of formation and functioning of layers, mechanisms of their interaction are considered. Based on analysis of the formation of control signals in the organization of motion control, the principles of intelligent information processing are formulated. Examples of the implementation of these principles in the intelligent control system are given. The results show the relevance of the scientific Bernstein’s principles for the development of intelligent system.


Neural networks Motion control levels Natural intelligence Intelligent control Human operator 


  1. 1.
    Proletarsky, A.V., Shen, K., Neusypin, K.A.: Intelligent control systems: contemporary problems in theory and implementation in practice. In: 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering, pp. 39–47 (2015)Google Scholar
  2. 2.
    Shen, K., Selezneva, M.S., Neusypin, K.A., Proletarsky, A.V.: Novel variable structure measurement system with intelligent components for flight vehicles. Metrol. Meas. Syst. 24(2), 347–356 (2017)CrossRefGoogle Scholar
  3. 3.
    Gugerty, L.: Newell and Simon’s logic theorist: historical background and impact on cognitive modeling. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 50(9), 880–884 (2006). Scholar
  4. 4.
    Mitchell, T.M., Michalski, R.S., Carbonell, J.G.: Machine Learning: An Artificial Intelligence Approach, vol. 1, 572 p. Elsevier (2014)Google Scholar
  5. 5.
    Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edn. Addison-Wesley Longman, London (2008)Google Scholar
  6. 6.
    Piccinini, G.: The first computational theory of mind and brain: a close look at Mcculloch and Pitts’s “Logical Calculus of Ideas Immanent in Nervous Activity”. Synthese 141, 175–215 (2004)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Pospíchal, J., Kvasnička, V.: 70th anniversary of publication: Warren McCulloch & Walter Pitts—a logical calculus of the ideas immanent in nervous activity. In: Sinčák, P., Hartono, P., Virčíková, M., Vaščák, J., Jakša, R. (eds.) Emergent Trends in Robotics and Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 316. Springer, Cham (2015). Scholar
  8. 8.
    Egiazaryan, G.G., Sudakov, K.V.: Theory of functional systems in the scientific school of P.K. Anokhin. J. History Neurosci. 16(1–2), 194–205 (2007)CrossRefGoogle Scholar
  9. 9.
    Novikov, D.: Cybernetics: From Past to Future, p. 107. Springer, Berlin (2016)CrossRefGoogle Scholar
  10. 10.
    Montagnini, L.: Wiener and Computers. Act 2. In: Harmonies of Disorder. Springer Biographies. Springer, Cham (2017). Scholar
  11. 11.
    Bernstein, N.A.: The current problems of modern neurophysiology. In: Sporns, O., Edelman, G.M. (eds.). Bernstein’s Dynamic View of the Brain: The Current Problems of Modem Neurophysiology. Motor Control, 2(4), 285–299. (1998). (Original work published 1945)Google Scholar
  12. 12.
    Labra-Spröhnle, F.: Human, all too human: Euclidean and multifractal analysis in an experimental diagrammatic model of thinking. Cogn. Syst. Monogr. 29, 105–133 (2016). Scholar
  13. 13.
    Bongaardt, R., Meijer, O.G.: Bernstein’s theory of movement behavior: historical development and contemporary relevance. J. Mot. Behav. 32(1), 57–71 (2000)CrossRefGoogle Scholar
  14. 14.
    Selezneva, M.S., Neusypin, K.A.: Development of a measurement complex with intelligent component. Meas. Tech. 59(9), 916–922 (2016)CrossRefGoogle Scholar
  15. 15.
    Buldakova, T.I., Suyatinov, S.I.: Registration and identification of pulse signal for medical diagnostics. In: Proceedings of SPIE—The International Society for Optical Engineering, vol. 4707, pp. 343–350 (2002). Paper 48Google Scholar
  16. 16.
    Valavanis, K.P., Saridis, G.N.: Intelligent Robotic Systems: Theory, Design and Applications. Springer, New York (2012)zbMATHGoogle Scholar
  17. 17.
    Forrest, J., Novikov, D.: Modern trends in control theory: networks, hierarchies and interdisciplinarity. Adv. Syst. Sci. Appl. 12(3), 1–13 (2012)Google Scholar
  18. 18.
    Vasil’ev, S.N., Doganovskij, S.A., Edemskij, V.M.: To the intelligent control of electric arc furnaces. Avtomatizacija v promyshlennosti (3), 39–43 (2003). (in Russian)Google Scholar
  19. 19.
    Suyatinov, S.I., Kolentev, S.V., Bouldakova, T.I.: Criteria of identification of the medical images. In: Proceedings of SPIE—The International Society for Optical Engineering, vol. 5067, pp. 148–153 (2002)Google Scholar
  20. 20.
    Lantsberg, A.V., Treusch, K., Buldakova, T.I.: Development of the electronic service system of a municipal clinic (based on the analysis of foreign web resources). Autom. Doc. Math. Linguist. 45(2), 74–80 (2011)CrossRefGoogle Scholar
  21. 21.
    Boucsein, W., Haarmann, A., Schaefer, F.: Combining skin conductance and heart rate variability for adaptive automation during simulated IFR flight. In: Harris, D. (ed.) EPCE 2007. LNCS, vol. 4562. Springer, Berlin (2007). Scholar
  22. 22.
    Kalinichenko, A.N., Yur’eva, O.D.: Assessment of human physchophysiological states based on methods for heart rate variability analysis. Pattern Recognit. Image Anal. 22(4), 570–575 (2012). Scholar
  23. 23.
    Lia, B.N., Fu, B.B., Donga, M.C.: Development of a mobile pulse waveform analyzer for cardiovascular health monitoring. Comput. Biol. Med. 38, 438–445 (2008)CrossRefGoogle Scholar
  24. 24.
    Geisler, F.C., Kubiak, T., Siewert, K., Weber, H.: Cardiac vagal tone is associated with social engagement and self-regulation. Biol. Psychol. 93(2), 279–286 (2013)CrossRefGoogle Scholar
  25. 25.
    Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28, R1–R39 (2007). Scholar
  26. 26.
    Gil, E., Orini, M., Bailon, R., Vergara, J., Mainardi, L., Laguna, P.: Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non- stationary conditions. Physiol. Meas. 31, 1271–1290 (2010)CrossRefGoogle Scholar
  27. 27.
    Poh, M.-Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011)CrossRefGoogle Scholar
  28. 28.
    Birrenkott, D.A., Pimentel, M.A.F., Watkinson, P.J., Clifton, D.A.: A robust fusion model for estimating respiratory rate from photoplethysmography and electrocardiography. IEEE Trans. Biomed. Eng. 65(9), 2033–2041 (2018). Scholar
  29. 29.
    Buldakova, T.I., Suyatinov, S.I.: Reconstruction method for data protection in telemedicine systems. In: Progress in Biomedical Optics and Imaging—Proceedings of SPIE, vol. 9448 (2014). Paper 94481U
  30. 30.
    Suyatinov, S.I.: The use of active learning in biotechnical engineering education. In: Smirnova, E., Clark, R. (eds.). Handbook of Research on Engineering Education in a Global Context, pp. 233–242. IGI Global, Hershey, PA (2019). Web. 23 Oct. 2018.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bauman Moscow State Technical UniversityMoscowRussia

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