Advertisement

Abstract Vocabulary as Base for Training with Pattern Recognition EMG Control

  • Erik HaringEmail author
  • Seth Van Akeleyen
  • Kristof Vaes
  • Steven Truijen
  • Stijn VerwulgenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 972)

Abstract

The uprising of multi-channel wearable EMG sensors combined with machine learning pattern recognition algorithms offers the possibility to control multiple degree of freedom hand prosthetics. Such human-machine interaction systems require training from the user, mostly to link gestures with underlying EMG patterns. As intended end users have a missing hand, the question arises how to train them to use myo-electric prosthetics without instructing them to perform gestures; A key element to start training with pattern recognition EMG based prosthetic control is creating a shared vocabulary with the participant/patient. The shared vocabulary forms the base for the explanation and communication about the pattern recognition EMG. In this research an abstract form of communication based on animal sounds is used to form a shared vocabulary for a child with missing hands. We found that the abstract communication worked well and motivating when explaining pattern recognition EMG to a child. The communication tool that gives additional interaction makes the explanation much clearer since the participant starts directly with experiencing the pattern recognition EMG. Also, it is concluded that the abstract nature of the tested communication allows the participant to keep an open mind for gestures other than normal healthy hand movements when exploring the possible control contractions. Thus, abstract based communication can offer benefits during the training with pattern recognition EMG.

Keywords

EMG Pattern recognition EMG Training Children Prosthetic control 

References

  1. 1.
    Belter, J.T., Segil, J.L., Dollar, A.M., Weir, R.F.: Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review. J. Rehabil. Res. Dev. 50(5), 599 (2013)CrossRefGoogle Scholar
  2. 2.
    Cordella, F., et al.: Literature review on needs of upper limb prosthesis users. Front. Neurosci. 10, 209 (2016)CrossRefGoogle Scholar
  3. 3.
    Chadwell, A., Kenney, L., Thies, S., Galpin, A., Head, J.: The reality of myoelectric prostheses: understanding what makes these devices difficult for some users to control. Front. Neurorobot. 10, 7 (2016)CrossRefGoogle Scholar
  4. 4.
    Kyberd, P.J., Chappell, P.H.: The Southampton Hand: an intelligent myoelectric prosthesis. J. Rehabil. Res. Dev. 31(4), 326–334 (1994)Google Scholar
  5. 5.
    Segil, J.L., Controzzi, M., Weir, R.F., Cipriani, C.: Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands. J. Rehabil. Res. Dev. 51(9), 1439–1454 (2014)CrossRefGoogle Scholar
  6. 6.
    Young, A.J., Smith, L.H., Rouse, E.J., Hargrove, L.J.: Classification of simultaneous movements using surface EMG pattern recognition. IEEE Trans. Biomed. Eng. 60(5), 1250–1258 (2013)CrossRefGoogle Scholar
  7. 7.
    Simon, A.M., Lock, B.A., Stubblefield, K.A.: Patient training for functional use of pattern recognition-controlled prostheses. J. Prosthet. Orthot. 24(2), 56–64 (2012)CrossRefGoogle Scholar
  8. 8.
    Thalmic Labs: Myo Gesture Control Armband—Wearable Technology by Thalmic Labs. https://www.myo.com/. Accessed 17 Oct 2017
  9. 9.
    Attenberger, A., Buchenrieder, K.: RemoteHand: a wireless myoelectric interface. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8511 LNCS, no. PART 2, pp. 3–11 (2014)CrossRefGoogle Scholar
  10. 10.
    Masson, S., Fortuna, F.S., Moura, F.S., Soriano, D.C.: Integrating Myo armband for the control of myoelectric upper limb prosthesisGoogle Scholar
  11. 11.
    Visconti, P., Gaetani, F., Zappatore, G.A., Primiceri, P.: Technical features and functionalities of Myo armband: an overview on related literature and advanced applications of myoelectric armbands mainly focused on arm prostheses. Int. J. Smart Sens. Intell. Syst. 11(1), 1–25 (2018)Google Scholar
  12. 12.
    Stubblefield, K., Finucane, S.B., Miller, L.A., Lock, B.A.: Training individuals to use pattern recognition to control an upper limb prosthesis. In: Proceedings of 2011 MyoElectric Control. Prosthetics Symposium, pp. 1–4 (2011)Google Scholar
  13. 13.
    Thalmic Labs: Myo Connect, SDK and firmware downloads – Welcome to Myo Support. https://support.getmyo.com/hc/en-us/articles/360018409792. Accessed 21 Jan 2019
  14. 14.
    EZ Robot: EZ-Builder for Windows - EZ-Robot. https://www.ez-robot.com/EZ-Builder/. Accessed 21 Jan 2019
  15. 15.
    Verwulgen, S., et al.: A proof of concept that stroke patients can steer a robotic system at paretic side with Myo-electric signals, pp. 181–188 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department Product DevelopmentUniversity of AntwerpAntwerpBelgium

Personalised recommendations