Fuzzy Bionic Hand Control in Real-Time Based on Electromyography Signal Analysis

  • Martin TabakovEmail author
  • Krzysztof Fonal
  • Raed A. Abd-Alhameed
  • Rami Qahwaji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)


In this paper a fuzzy model for control of bionic hand in real-time is proposed. The control process involves interpretation and analysis of surface electromyography signal (sEMG) acquired from patients with amputees. The work considers the use of force sensing resistor to achieve better control of the artificial hand. The classical type-1 Mamdani fuzzy control model is considered for this application. The conducted experiments show comparable results with respect to applied assumptions that give the confidence to implement the proposed concept into real-time control process.


Fuzzy control Mamdani model Bionic limbs Electromyography Signal analysis 



This work was supported by the statutory funds of the Department of Computational Intelligence, Faculty of Computer Science and Managament, Wroclaw University of Science and Technology and partially by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE European Research Centre of Network Intelligence for Innovation Enhancement ( All computer experiments were carried out using computer equipment sponsored by ENGINE project. Additionally, the authors would like to thank the Wroclaw City Council, for the opportunity to work with the Neuro-Rehabilitation Center for the Treatment of Spinal Cord Injuries ‘Akson’, under the ‘Mozart’ city programme 2014/2015.


  1. 1.
    Bronstein, I.N., Semendjajew., K.A. Musiol, G., Mühlig, H.: Taschenbuch der Mathematik, Verlag Harri Deutsch, p. 1258 (2001)Google Scholar
  2. 2.
    Chatterjee, A., Chatterjee, R.: Matsuno., F., Endo, T.: Augmented stable fuzzy control for flexible robotic arm using LMI approach and neurofuzzy state space modeling. IEEE Trans. Ind. Electron. 55(3), 1256–1270 (2008)CrossRefGoogle Scholar
  3. 3.
    Crawford, B., Miller, K.J., Shenoy, P., Rao, R.P.N.: Real-time classification of electromyographic signals for robotic control. In: National Conference on Artificial Intelligence - AAAI, pp. 523–528 (2005)Google Scholar
  4. 4.
    Cram, J.R., Kasman, G.: Introduction to Surface Electromyography. Aspen Publishing, Gaterburg (1998)Google Scholar
  5. 5.
    Gauthaam, M., Kumar, S.S.: EMG controlled bionic arm. In: Proceedings of the National Conference on Innovations in Emerging Technology-2011 Kongu Engineering College, Perundurai, Erode, Tamilnadu, India, 17 & 18 February, pp. 111–114 (2011)Google Scholar
  6. 6.
    Ielpo, N., Calabrese, B., Cannataro, M., Palumbo, A., Ciliberti, S., Grillo, C., Iocco, M.: EMG-miner: automatic acquisition and processing of electromyographic signals: first experimentation in a clinical context for gait disorders evaluation. In: Proceedings of the IEEE 27th International Symposium on Computer-Based Medical Systems, pp. 441– 446 (2014)Google Scholar
  7. 7.
    Konrad, P.: The ABC of EMG: a practical introduction to kinesiological electromyography, Version 1.0 April. Noraxon INC., US (2005)Google Scholar
  8. 8.
    Lea, R.N., lani, Y., Hoblit, J.: Fuzzy logc based robotic arm control. In: Proceedings of the Second IEEE ICFS, SF, CA, vol. 1, pp. 128–133 (1993)Google Scholar
  9. 9.
    Li, M., Jiang, Z., Wang, P., Sun, L., Ge, S.S.: Control of a quadruped robot with bionic springy legs in trotting gait. J. Bionic Eng. 11(2), 188–198 (2014)CrossRefGoogle Scholar
  10. 10.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mac Stud. 7(1), 1–13 (1975)CrossRefzbMATHGoogle Scholar
  11. 11.
    Massa, B., Roccella, S., Carrozza, M.C., Dario, P.: Design and development of an underactuated prosthetic hand. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Washington DC, May 11–15, pp. 3374–3379 (2002)Google Scholar
  12. 12.
    Pedreira, C., Martinez, J., Quiroga, R.Q.: Neural prostheses: linking brain signals to prosthetic devices. In: Proceedings on the ICROS-SICE International joint conference, Fukuoka, Japan, August (2009)Google Scholar
  13. 13.
    Shenoy, P., Miller, K.J., Crawford, B., Rao, R.P.N.: Online electromyographic control of a robotic prosthesis. IEEE Trans. Biomed. Eng. 55(3), 1128–1135 (2008)CrossRefGoogle Scholar
  14. 14.
    Shekhar, H., Guha, R., Juliet, A.V., Sam, J., Kumar, J.: Mathematical modeling of neuro-controlled bionic. In: Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing, pp. 576 – 578 (2009)Google Scholar
  15. 15.
    Spanias, J.A., Simon, A.M.K., Ingraham, A., Hargrove, L.J.: Effect of additional mechanical sensor data on an EMG-based pattern recognition system for a powered leg prosthesis. In: Proceedings of the 7th International IEEE/EMBS Conference on Neural Engineering (NER), Montpellier, pp. 639 – 642 (2015)Google Scholar
  16. 16.
    Tomas, S., Michal, K., Alena, K.: Fuzzy control of robotic arm implemented in PLC. In: Proceedings of the IEEE 9th International Conference on Computational Cybernetics (ICCC), pp. 45 – 49, Tihany (2013)Google Scholar
  17. 17.
    Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Zhuojun, X., Yantao, T., Yang, L.: sEMG pattern recognition of muscle force of upper arm for intelligent bionic limb control. J. Bionic Eng. 12, 316–323 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Martin Tabakov
    • 1
    Email author
  • Krzysztof Fonal
    • 1
  • Raed A. Abd-Alhameed
    • 2
  • Rami Qahwaji
    • 2
  1. 1.Department of Computational IntelligenceWroclaw University of Science and TechnologyWroclawPoland
  2. 2.School of Electrical and Computer ScienceUniversity of BradfordBradfordUK

Personalised recommendations