Abstract
Control approaches for the most modern exoskeleton devices are based on the use of the potentiometric proportional sensors. This allows setting the velocity of the movement of the exoskeleton links, but has significant peculiarities, which are concluded in a large time delay for processing the control signal and increased sensitivity of such sensors, which leads to increased injury risk during control. The use of muscle biopotentials for control of an exoskeleton device also makes it possible to take into account the physiological characteristics of the operator for using the exoskeleton in various areas of human activity. The development of control algorithms of the exoskeleton, along with the use of the activity of human muscle groups’ data, is essential for expanding the functionality of a human-machine system such as the “operator-exoskeleton”. The paper considers the interaction of a human and an exoskeleton drive based on mathematical models of a DC motor with a current feedback loop and a muscle duplex. A calibration algorithm is proposed to determine the parameters of the muscle duplex model in order to form a database that corresponds to an individual operator and reflects its individual characteristics. The technique for setting the parameters of the control system in the exoskeleton calibration mode is given. Paper presents the results of experiments with the developed algorithm on full-scale stand, simulating the arm exoskeleton with the electric drive, located in the elbow joint and controlling algorithms based on the electromyogram of the biceps brachii and triceps brachii of the operator. The structure and features of the stand developed in the laboratory of robotics and mechatronics of IPMech RAS are shown. A comparative characteristic of the control quality of the electric drive, which is part of the exoskeleton, with the proposed algorithm in relation to one operator when changing by another one was worked out. At the same time, the following control quality indicators were evaluated – over-regulation, time to set the specified position, and accuracy of positioning the control point of the exoskeleton link. The present work was supported by the Ministry of Science and Higher Education within the framework of the Russian State Assignment under contract No. AAAA-A20-120011690138-6.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rukina, N.N., Kuznetsov, A.N., Borzikov, V.V., Komkova, O.V., Belova, A.N.: Surface electromyography: its role and potential in the development of exoskeleton (review). Sovremennye tehnologii v medicine 8(2), 109–118 (2016). https://doi.org/10.17691/stm2016.8.2.15
Renato, V., Matamala, J.: Clinical neurophysiology standards of EMG instrumentation: twenty years of changes. Clin. Neurophysiol. 131 (2019). https://doi.org/10.1016/j.clinph.2019.08.023
Cifrek, M., Medved, V., Tonkovic, S., Ostojić, S.: Surface EMG based muscle fatigue evaluation in biomechanics. Clin. Biomech. (Bristol, Avon) 24, 327–340 (2009). https://doi.org/10.1016/j.clinbiomech.2009.01.010
Dimitrova, N.A., Dimitrov, G.V.: Interpretation of EMG changes with fatigue: facts, pitfalls, and fallacies. J. Electromyogr. Kines. 13(1), 13–36 (2003)
De Luca, C.J.: Spectral compression of the EMG signal as an index of muscle fatigue. In: Sargeant, A.J., Kernell, D. (eds.) Neuromuscular Fatigue, pp. 44–51. Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands (1992)
De Sapio, V.: An approach for goal-oriented neuromuscular control of digital humans in physics-based simulations. Int. J. Hum. Fact. Model. Simul. 4, 121–144 (2014). https://doi.org/10.1504/IJHFMS.2014.062387
Valderrabano, V., et al.: Muscular lower leg asymmetry in middle-aged people. Foot Ankle Int. 28(2), 242–249 (2007). https://doi.org/10.3113/FAI.2007.0242
Gradetsky, V.G., Ermolov, I.L., Knyazkov, M.M., Semenov, E.A., Sukhanov, A.N.: Switching operation modes algorithm for the exoskeleton device. In: Gorodetskiy, A.E., Tarasova, I.L. (eds.) Smart Electromechanical Systems. SSDC, vol. 261, pp. 131–142. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32710-1_10
Gradetsky, V., Ermolov, I., Knyazkov, M., Semenov, E., Sukhanov, A.: Features of human-exoskeleton interaction. In: Studies in Systems, Decision and Control, volume 261 of Robotics: Industry 4.0 Issues & New Intelligent Control Paradigms, pp. 77–88. Springer Nature Switzerland, Switzerland (2020)
Gradetsky, V., Ermolov, I., Knyazkov, M., Semenov, E., Sukhanov, A.: Osobennosti proektirovaniya aktivnoj ekzoskeletnoj sistemy s bioupravle-niem. In: Trudy XIII Vserossijskogo soveshchaniya po problemam upravleniya (VSPU-2019) 17–20 iyunya 2019 g. Moskva, IPU RAN, ISBN 978-5-91450-234-5, № gos. registracii: 0321902409, pp. 801–805. IPU RAN Moskva (2019)
Fuentes, S., Santos-Cuadros, S., Olmeda, E., Álvarez-Caldas, C., Díaz, V., San Román, J.: Is the use of a low-cost sEMG sensor valid to measure muscle fatigue? Sensors 19, 3204 (2019). https://doi.org/10.3390/s19143204
Andersson, S.: Active Muscle Control in Human Body Model Simulations. Master’s Thesis in Automotive Engineering, CHALMERS, Applied Mechanics, 62, p. 64 (2013)
Zajac, F.E., Gordon, M.E.: Determining muscle’s force and action in multi-articular movement. Exerc. Sport Sci. Rev. 17, 187–230 (1989) and Zajac, F.E. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. CRC Crit. Rev. Biomed. Eng. 17, 359–411 (1989)
Sancho-Bru, J.L., Pérez-González, A., Mora, M.C., León, B.E., Vergara, M., Iserte, J.L., et al.: Towards a realistic and self-contained biomechanical model of the hand (2011)
Wilkie, D.R.: The mechanical properties of muscle. Br. Med. Bull. 12 (1956)
Novoselov, V.S.: On mathematical models of molecular contraction of skeletal muscles. Vestnik SPbGU. Ser. 3 88–96 (2016) (in Russian)
Grosu, V., Rodriguez-Guerrero, C., Brackx, B., Grosu, S., Vanderborght, B., Lefeber, D.: Instrumenting complex exoskeletons for improved human-robot interaction. Instrum. Meas. Mag. IEEE 18, 5–10 (2015). https://doi.org/10.1109/MIM.2015.7271219
Matthew, W., et al.: Muscular activity and physical interaction forces during lower limb exoskeleton use. Healthc. Technol. Lett. 3(4), 273–279 (2016)
Sado, F., Yap, H.J., Ghazilla, R.A.R., Ahmad, N.: Exoskeleton robot control for synchronous walking assistance in repetitive manual handling works based on dual unscented Kalman filter. PLoS ONE 13(7), e0200193 (2018). https://doi.org/10.1371/journal.pone.0200193
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gradetsky, V.G., Ermolov, I.L., Knyazkov, M.M., Semenov, E.A., Sukhanov, A.N. (2021). Control System Calibration Algorithm for Exoskeleton Under the Individual Specificities of the Operator. In: Yuschenko, A. (eds) Modern Problems of Robotics. MPoR 2020. Communications in Computer and Information Science, vol 1426. Springer, Cham. https://doi.org/10.1007/978-3-030-88458-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-88458-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88457-4
Online ISBN: 978-3-030-88458-1
eBook Packages: Computer ScienceComputer Science (R0)