Adaptive biotechnical system with a robotic device for the restoration of motor functions of the lower extremities in post-stroke patients

To restore the motor functions of the lower extremities in post-stroke patients, it is proposed to use a biotechnical system with a robotic device. The control is based on the analysis and classification of electromyosignals. The robotic device is controlled by a fuzzy control module, which allows maintaining three modes of rehabilitation, selecting and switching them depending on the functional state of the patient, thereby deciding on the optimal rehabilitation program for the current functional state of the patient. The control model includes three fuzzy control modules with the corresponding bases of fuzzy decision rules and it allows you to adapt the rehabilitation procedure to the functional state of the patient. To assess the effectiveness of the proposed method of rehabilitation, the experimental group included 23 patients who underwent exacerbations from 25 days to 5 years, including patients with subacute (<180 days after exacerbation) and chronic (> 180 days after exacerbation) conditions. After a course of rehabilitation by means of a biotechnical system with a fuzzy control module, there is a significant increase in the maxima of the support reaction force Rz on the affected leg in the experimental group in relation to the control group. Accordingly, the amplitude of the front push in the experimental group increased by 62% (120%), the rear push by 58% (115%), while in the control group the amplitude increase was 40% (101%) and 41% (105 %). In this case, distinct maxima of the support reaction component Rz appear on the paretic leg.

1. Al'-Bareda A.Ya.S., Brezhneva A.N., Tomakova R.A. Algorithms for the synthesis of optimal control in biotechnical systems of rehabilitation type based on neural network technologies. Sistemnyi analiz i upravlenie v biomeditsinskikh sistemakh. 2018;17(3):750-754. (In Russ)

2. Trifonov A.A, Petrunina E.V., Filist S.A., Kuz'min A.A., Zhilin V.V. Biotechnical system with virtual reality in rehabilitation complexes with artificial feedbacks. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie. 2019;9(4):46-66. (In Russ)

3. Trifonov A., Filist S., Degtyarev S., Serebrovsky V., and Shatalova O. Human–Machine Interface of Rehabilitation Exoskeletons with Redundant Electromyographic Channels. Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”. Springer, Singapore, 2021;237-247. DOI: https://doi.org/10.1007/978-981-15-5580-0_19.

4. Filist S.A., Shatalova O.V., Efremov M.A. Hybrid neural network with macro layers for medical applications. Neurocomputers. 2014;6:35-39. (In Russ)

5. Efremov M.A., Shatalova O.V., Fedyanin V.V., Shutkin A.N. Hybrid multi-agent classifiers in biotechnical systems for the diagnosis of diseases and monitoring of medicinal prescriptions. Neurocomputers. 2015;6:42-47. (In Russ)

6. Petrova T.V., Filist S.A., Degtyarev S.V., Kiselev A.V., Shatalova O.V. Predictors of synchronicity of system rhythms of living systems for classifiers of their functional states. Sistemnyi analiz i upravlenie v biomeditsinskikh sistemakh. 2018;17(3):693-700. (In Russ)

7. Filist S.A., Petrunina E.V., Trifonov A.A., Serebrovskii A.V. Electroencephalogram signal codes for controlling robotic devices through a brain-computer interface. Modeling, Optimization and Information Technology. 2019;7(1). Available at: https://moitvivt.ru/ru/journal/pdf?id=555. DOI: 10.26102/2310-6018/2019.24.1.025 (accessed 10.08.2021). (In Russ)

8. Trifonov A.A., Kuzmin A.A., Filist S. A. and Petrunina E.V. Neural network model in the exoscelete verticalization control system. Journal of Phisics: Conference Series. 2020;1679(3). Available at: https://iopscience.iop.org/article/10.1088/1742-6596/1679/3/032036/pdf. DOI:10.1088/1742-6596/1679/3/032036 (accessed 15.08.2021).

9. Trifonov A.A., Filist S.A., Kuz'min A.A., Zhilin V.V., Petrunina E.V. A two-level neural network model of an electromyosignal decoder in an exoskeleton verticalization control system. Prikaspiiskii zhurnal: upravlenie i vysokie tekhnologii, 2020;(4):99-111. (In Russ)

10. Trifonov A.A., Kuz'min A.A., Petrunina E.V., Kadyrova S. Means of assessing muscle load and muscle fatigue for controlling the exoskeleton in a combined mode. Lazery. Izmereniya. Informatsiya. 2021;1(1):55-66.