Radioelectronics and Communications Systems

, Volume 62, Issue 1, pp 23–33 | Cite as

Fingers Movements Control System Based on Artificial Neural Network Model

  • Kostiantyn VonsevychEmail author
  • Márcio Fagundes GoethelEmail author
  • Jerzy MrozowskiEmail author
  • Jan AwrejcewiczEmail author
  • Mikhail BezuglyiEmail author


Surface electromyographic (sEMG) signal is used in the various fields of applications where the need exists to measure the activity of body muscles, such as brain-computer interfaces, game industry, medical engineering, and other practical spheres. Even more, the use of sEMG signal in the field of active prosthesis industry has become traditional for many years. However, despite the fact that the question of using it in the field of fingers prostheses is still open, in general, the sEMG signal required multichannel measuring devices or massive, voluminous equipment for precise recognition of hands or fingers movement. That is decreasing the possible portability and convenience of prostheses and as a consequence is increasing their final price. In this paper we propose a method of organizing the controlling and measuring unit of the prosthetic device based on artificial neural network (ANN) model and one-channel microcontroller based sEMG measuring system. The proposed ANN model works with only 4 input time-domain features of sEMG signal and provides an accuracy of 95.52% for classification of 6 different types of finger movements that makes it a good solution for next implementation in the system of prosthetic fingers or wrist devices.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    T.–W. Chien, W.–S. Lin, “Simulation study of activities of daily living functions using online computerized adaptive testing,” BMC Med. Inform. Decis. Mak. 16, 130 (2016). DOI: Scholar
  2. 2.
    P. Gulde, J. Hermsdörfer, “Both hands at work: the effect of aging on upper–limb kinematics in a multi–step activity of daily living,” Exp. Brain Res. 235, No. 5, 1337 (2017). DOI: Scholar
  3. 3.
    L. Resnik, M. Borgia, F. Acluche, “Timed activity performance in persons with upper limb amputation: A preliminary study,” J. Hand Ther. 30, No. 4, 468 (2017). DOI: Scholar
  4. 4.
    J. M. Zuniga, A. M. Carson, J. M. Peck, T. Kalina, R. M. Srivastava, K. Peck, “The development of a low–cost three–dimensional printed shoulder, arm, and hand prostheses for children,” Prosthet. Orthot. Int. 41, No. 2, 205 (2017). DOI: Scholar
  5. 5.
    F. Cordella, A. L. Ciancio, R. Sacchetti, A. Davalli, A. G. Cutti, E. Guglielmelli, L. Zollo, “Literature review on needs of upper limb prosthesis users,” Front. Neurosci. 10, 1 (2016). DOI: Scholar
  6. 6.
    S. G. Postema, R. M. Bongers, M. A. Brouwers, H. Burger, L. M. Norling–Hermansson, M. F. Reneman, P. U. Dijkstra, C. K. Van der Sluis, “Upper limb absence: predictors of work participation and work productivity,” Arch. Phys. Med. Rehabil. 97, 892 (2016). DOI: Scholar
  7. 7.
    H. Burger, G. Vidmar, “A survey of overuse problems in patients with acquired or congenital upper limb deficiency,” Prosthet. Orthot. Int. 40, 497 (2016). DOI: Scholar
  8. 8.
    C. Widehammar, I. Pettersson, G. Janeslätt, L. Hermansson, “The influence of environment: Experiences of users of myoelectric arm prosthesis—a qualitative study,” Prosthet. Orthot. Int. 42, No. 1, 28 (2018). DOI: Scholar
  9. 9.
    A. Arabian, D. Varotsis, C. McDonnell, E. Meeks, “Global social acceptance of prosthetic devices,” Proc. of IEEE Glob. Humanit. Technol. Conf., 13–16 Oct 2016, Seattle, USA (IEEE, 2016), pp. 563–568. DOI: Scholar
  10. 10.
    S. G. Postema, R. M. Bongers, M. F. Reneman, C. K. Van Der Sluis, “Functional capacity evaluation in upper limb reduction deficiency and amputation: Development and pilot testing,” J. Occup. Rehabil. 28, No. 1, 158 (2018). DOI: Scholar
  11. 11.
    K. V. Wong, A. Hernandez, “A review of additive manufacturing,” ISRN Mechanical Engineering 2012, ID 208760, 1 (2012). DOI: Scholar
  12. 12.
    J. T. Kate, G. Smit, P. Breedveld, “3D–printed upper limb prostheses: a review,” Disability and Rehabilitation: Assistive Technology 12, No. 3, 300 (2017). DOI: Scholar
  13. 13.
    J. Koprnicky, P. Najman, J. Safka, “3D printed bionic prosthetic hands,” Proc. of 2017 IEEE Int. Workshop on Electronics, Control, Measurement, Signals and their Application to Mechatronics, ECMSM, 24–26 May 2017, Donostia–San Sebastian, Spain (IEEE, 2017), pp. 1–6. DOI: Scholar
  14. 14.
    M. Atzori, H. Müller, “Control capabilities of myoelectric robotic prostheses by hand amputees: a scientific research and market overview,” Front. Syst. Neurosci. 9, 1 (2015). DOI: Scholar
  15. 15.
    B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci, “The psychophysiology primer: a guide to methods and a broad review with a focus on human–computer interaction,” in: Foundations and Trends in Human–Computer Interaction 9, No. 3–4, 150 (2016). DOI: Scholar
  16. 16.
    W. Ma, X. Zhang, G. Yin, “Design on intelligent perception system for lower limb rehabilitation exoskeleton robot,” Proc. of IEEE 13th Int. Conf. on Ubiquitous Robot and Ambient Intelligence, 19–22 Aug 2016, Xian, China (IEEE, 2016), pp. 587–592. DOI: Scholar
  17. 17.
    S. Sharma, H. Farooq, N. Chahal, “Feature extraction and classification of surface EMG signals for robotic hand simulation,” Commun. Appl. Electron. 4, 27 (2016). DOI: Scholar
  18. 18.
    J. A. Spanias, E. J. Perreault, L. J. Hargrove, “Detection of and compensation for EMG disturbances for powered lower limb prosthesis control,” IEEE Trans. Neural Syst. Rehabil. Eng. 24, No. 2, 226 (2016). DOI: Scholar
  19. 19.
    A. Gailey, P. Artemiadis, M. Santello, “Proof of concept of an online EMG–based decoding of hand postures and individual digit forces for prosthetic hand control,” Front. Neurol. 8, 1 (2017). DOI: Scholar
  20. 20.
    Y. Na, S. J. Kim, S. Jo, J. Kim, “Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure,” Med. Biol. Eng. Comput. 55, No. 8, 1507 (2017). DOI: Scholar
  21. 21.
    M. Ariyanto, W. Caesarendra, K. A. Mustaqim, M. Irfan, J. A. Pakpahan, J. D. Setiawan, A. R. Winoto, “Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor,” Proc. of Int. Conf. on Automation, Cognitive Science, Optics, Micro Electro–Mechanical System, and Information Technology, ICACOMIT, 29–30 Oct 2015, Bandung, Indonesia (IEEE, 2015), pp. 12–17. DOI: Scholar
  22. 22.
    F. V. G. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne–Cummings, N. V. Thakor, “Decoding of individuated finger movements using surface electromyography,” IEEE Trans. Biomed. Eng. 56, No. 5, 1427 (2009). DOI: Scholar
  23. 23.
    M. Zecca, S. Micera, M. C. Carrozza, P. Dario, “Control of multifunctional prosthetic hands by processing the electromyographic signal,” Crit. Rev. Biomed. Eng. 30, No. 4–6, 459 (2002). DOI:
  24. 24.
    S. Micera, J. Carpaneto, S. Raspopovic, “Control of hand prosthesis using peripheral information,” IEEE Rev. Biomed. Eng. 3, 48 (2010). DOI: Scholar
  25. 25.
    I. Strazzulla, M. Nowak, M. Controzzi, C. Cipriani, C. Castellini, “Online bimanual manipulation using surface electromyography and incremental learning,” IEEE Trans. Neural Syst. Rehabil. Eng. 25, No. 3, 227 (2017). DOI: Scholar
  26. 26.
    M. Tavakoli, C. Benussi, J. L. Lourenco, “Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach,” Expert Syst. Appl. 79, 322 (2017). DOI: Scholar
  27. 27.
    K. P. Vonsevych, M. O. Bezuglyi, A. O. Haponiuk, “Information–measuring system of myograph of bionic limb prosthesis,” Perspectyvni Tekhnologii ta Prylady 10, 32 (2017).Google Scholar
  28. 28.
    M. Heiderich, S. Leonhardt, W. Krantz, J. Neubeck, J. Wiedemann, “Method for analysing the feeling of safety at high speed using virtual test drives,” Proc. of 18 Internationales Stuttgarter Symp. (Springer Vieweg, Wiesbaden, 2018), pp. 875–886. DOI: Scholar
  29. 29.
    A. Horwitz, “A version of Simpson’s rule for multiple integrals,” J. Computational Applied Math. 134, No. 1–2, 1 (2001). DOI: Scholar
  30. 30.
    K. Levenberg, “A method for the solution of certain non–linear problems in least squares,” Q. Appl. Math. 2, No. 2, 164 (1944). URI: Scholar
  31. 31.
    D. W. Marquardt, “An algorithm for least–squares estimation of nonlinear parameters,” J. Soc. Ind. Appl. Math. 11, No. 2, 431 (1963). DOI: Scholar
  32. 32.
    D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning representations by back–propagating errors,” Nature 323, 533 (1986). DOI: Scholar
  33. 33.
    J. A. Swets, “Measuring the accuracy of diagnostic systems,” Science 240, No. 4857, 1285 (1988). DOI: Scholar
  34. 34.
    S. Kim, J. Kim, S. Ahn, Y. Kim, “Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors,” Technology Health Care 26, No. S1, 249 (2018). DOI: Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KyivUkraine
  2. 2.University of São PauloSão PauloBrazil
  3. 3.Technical University of LodzLodzPoland

Personalised recommendations