Authors A.V. Bakhshiev, E.Yu. Smirnova, P.E. Musienko
Month, Year 10, 2015 @en
Index UDC 004.942+004.896
Abstract Currently, the problems of limited mobility persons rehabilitation are very relevant (the limited mobility occurs due to spinal injuries and spinal cord injuries, stroke and other neuromotor diseases). The loss of mobility, inability to maintain body weight and balance, etc. causes numerous somatic disorders, contributes to the progression of urological, venous, cardiovascular disorders. Existing neurorehabilitation systems designed to compensate movement disorders, are based on passive springy sup-port, balancing mechanisms, or force control, which generate vertical forces at the level of the trunk while driving on the treadmill belt. Diseases and injuries of the brain and spinal cord are often accompanied by disturbances of postural and locomotor functions. To restore the active movement in space there are needed neurorehabilitation activities, aimed at both components of physical activity. Allocation of these sub-functions is of great importance to as- sess violations and subsequent locomotor neurorehabilitation. Conceptually, neurorehabilitation systems should act as a neuroprosthesis acting both on the motor (propulsion) properties, and the ability to maintain balance. We describe the algorithmic structure of the project to develop containment system of dynamic balance stabilization (exobalanсer). The system should ensure the neurorehabilitation of people with limited mobility and impaired balance maintenance, which occur due to neuromotor disorders. The reflex pathways and structure of spinal neural network control system underlying the control of movements and balance are discussed.

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Keywords Exobalancer; exoskeleton; neural network; motor control; neuromotor disorders; balance control; neurorehabilitation.
References 1. Thuret S., Moon L.D.F., Gage F.H. Therapeutic interventions after spinal cord injury, Nature Reviews Neuroscience, 2006, Vol. 7, No. 8, pp. 628-643.
2. Frey M. et al. A novel mechatronic body weight support system, IEEE Trans. Neural. Syst. Rehabil. Eng., 2006, Vol. 14, pp. 311-321.
3. Winter D.A., MacKinnon C.D., Ruder G.K. & Wieman C. An integrated EMG/biomechanical model of upper body balance and posture during human gait, Progress in brain research, 1992, Vol. 97, pp. 359-367.
4. Dominici N., Keller U., Vallery H., Friedli L., van den Brand R., Starkey M.L., Musienko P., Riener R., Courtine G. Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders, Nature medicine, 2012, Vol. 18, No. 7, pp. 1142-1147.
5. Dubrovskiy V.I., Fedorova V.N. Biomekhanika [Biomechanics]. Moscow: Izd-vo: VLADOS-PRESS, 2003. ISBN: 5-305-00101-3.
6. Preuschoft Н., Witte Н., Demes В. Biomechanical factors that influence overall body shape of large apes and humans, Topics in Primatology (ed.S. Matano, R. H. Tuttle, H. Ishida, and M. Goodman), Vol. 3, pp. 259-289. Tokyo: University of Tokyo Press, 1992.
7. Narvydas G., Simutis R. and Raudonis V. Autonomous mobile robot control using fuzzy logic and genetic algorithm, IEEE Conference Proceedings, IDAACS, 2007, pp. 460-464.
8. Sigeru O. Neyroupravlenie i ego prilozheniya [Nanoprene and its applications]: Translation from English. N.V. Bantina. Moscow: IPRZhR, 2000, 272 p.
9. Werbos P.J. Neurocontrol and fuzzy logic: connections and design, Int. J. Approximate Reasoning, Feb. 1992, Vol. 6, pp. 185-220.
10. Rotshteyn A.P. Intellektual'nye tekhnologii identifikatsii: nechetkaya logika, gene-ticheskie algoritmy, neyronnye seti [Intelligent identification technologies: fuzzy logic, genetic algorithms, neural networks]. Vinnitsa: UNIVERSUM-Vinnitsa, 1999, 320 p.
11. Hodgkin A.L., Huxley A.F. A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiology, 1952, No. 117, pp. 500-544.
12. Romanov S.P. Model' neyrona [The model neuron], Nekotorye problemy biologicheskoy kibernetiki [Some Problems of Biological Cybernetics], 1972, pp. 276-282.
13. Bakhshiev A.V. Perspektivy primeneniya modeley biologicheskikh neyronnykh struktur v sistemakh upravleniya dvizheniem [Prospects of application of models of biological neural structures in the traffic control systems], Informatsionno-izmeritel'nye i upravlyayushchie sistemy [Information-measuring and Control Systems], 2011, No. 9, pp. 71-80.
14. McKinstry J.L., Edelman G.M. и Krichmar J.L. A cerebllar model for predictive motor control tested in a brain-based device, Vol. 103 # 9, PNAS, February 28, 2006, pp. 3387-3392.
15. Hugo de Garis, Chen Shuo, Ben Goertzel, Lian Ruiting. A world survey of artificial brain projects. Part I: Large-scale brain simulations, Neurocomputing, 2010, Vol. 74, pp. 3-29.
16. Goertzel B., Ruiting L., Arel I., Garis H.d. и Shuo C. A world survey of artificial brain pro-
jects, Part II: Biologically inspired cognitive architectures, Neurocomputing, 2010, Vol. 74, pp. 30-49.
17. Romanov S.P. Strukturnoe obosnovanie funktsii nervnoy sistemy kak avtomaticheskogo regulyatora [Structural substantiation of the function of the nervous system as an automatic controller], Neyrokomp'yutery: razrabotka, primenenie [Neurocomputers: Development, Application], 2006, No. 7, pp. 54-63.
18. Romanov S.P. Neyrosistemy i sovremennye vychislitel'nye sredy [Nanosistemy and modern computing environment], Neyrokomp'yutery: razrabotka, primenenie [Neurocomputers: Development, Application], 2007, No. 6, pp. 96-104.
19. Bakhshiev A.V. Model' neyrona so strukturnoy adaptatsiey dendritnogo apparata dlya modelirovaniya estestvennykh neyronnykh setey upravleniya dvizheniem [Model of a neuron with a dendritic structural adaptation of the device to simulate a natural neural network motion control], Neyroinformatika, ee prilozheniya i analiz dannykh: Materialy XVIII Vseross. seminara, 27-29 sentyabrya 2013 g. [Narainpur-MATIC, its application and data analysis: Proceedings of the XVIII all-Russian. workshop, 27-29 September 2013], ed. by A.N. Gorbanya, E.M. Mirkesa. Krasnoyarsk, 2013, pp. 36-43.
20. Bakhshiev A.V., Gundelakh F.V. Issledovanie biopodobnoy modeli neyronnoy seti dlya upravleniya dvizheniem robototekhnicheskikh sistem [Biosimilar study a neural network model for motion control of robotic systems], Robototekhnika i iskusstvennyy intellekt: materialy VI Vserossiyskoy nauchno-tekhnicheskoy konferentsii s mezhdunarodnym uchastiem (g. Zheleznogorsk, 13 dekabrya 2014 g.) [Robotics and artificial intelligence: proceedings of the VI all-Russian scientific-technical conference with international participation (Zheleznogorsk, December 13, 2014)], ed. by V.A. Ugleva. Krasnoyarsk: Tsentr informatsii, TsNI «Monografiya», 2014, pp. 164-169.
21. Bakhshiev A.V., Gundelakh F.V. Issledovanie metoda zapominaniya prostranstvennykh konfiguratsiy robototekhnicheskoy sistemy na neyronnykh setyakh so strukturnoy adaptatsiey [Study of method of memorizing the spatial con-configurations of the robotic system on neural network with structural adaptation], Robototekhnika i tekhnicheskaya kibernetika [Robotics and Technical Cybernetics], 2015, No. 3 (8), pp. 46-51.

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