GMM-Based Single-Joint Angle Estimation Using EMG Signals

  • Stefano Michieletto
  • Luca Tonin
  • Mauro Antonello
  • Roberto Bortoletto
  • Fabiola Spolaor
  • Enrico Pagello
  • Emanuele Menegatti
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

This paper aims to explore the possibility to use Electromyography (EMG) to train a Gaussian Mixture Model (GMM) in order to estimate the bending angle of a single human joint. In particular, EMG signals from eight leg muscles and the knee joint angle are acquired during a kick task from three different subjects. GMM is validated on new unseen data and the classification performances are compared with respect to the number of EMG channels and the number of collected trials used during the training phase. Achieved results show that our framework is able to obtain high performances even using few EMG channels and with a small training dataset (Normalized Mean Square Error: 0.96, 0.98, 0.98 for the three subjects, respectively), opening new and interesting perspectives for the hybrid control of humanoid robots and exoskeletons.

Keywords

EMG signals Gaussian mixture model Gaussian mixture regression Single-joint angle estimation 
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Notes

Acknowledgments

This research has been supported by “Consorzio Ethics” through a grant for research activity on the project “Rehabilitation Robotics”.

References

  1. 1.
    Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory. pp. 267–281 (1973)Google Scholar
  2. 2.
    Artemiadis, P.K., Kyriakopoulos, K.J.: A bio-inspired filtering framework for the EMG-based control of robots. In: IEEE 17th Mediterranean Conference on Control and Automation. pp. 1155–1160 (2009)Google Scholar
  3. 3.
    Arvetti, M., Gini, G., Folgheraiter, M.: Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis. In: IEEE 10th International Conference on Rehabilitation Robotics. pp. 531–536 (2007)Google Scholar
  4. 4.
    Barron, A., Rissanen, J., Yu, B.: The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory 44(6), 2743–2760 (1998)Google Scholar
  5. 5.
    Calinon, S., Billard, A.: Active Teaching in Robot Programming by Demonstration. Robot & Human Interactive Communication pp. 702–707 (2007)Google Scholar
  6. 6.
    Calinon, S., Billard, A.: A probabilistic Programming by Demonstration framework handling constraints in joint space and task space. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 367–372 (2008)Google Scholar
  7. 7.
    Chowdhury, R., Reaz, M., Ali, M., Bakar, A., Chellappan, K., Chang, T.: Surface electromyography signal processing and classification techniques. Sensors 13(9), 12431–12466 (2013)Google Scholar
  8. 8.
    Chu, J., Lee, Y.J.: Conjugate prior penalized learning of Gaussian mixture models for EMG pattern recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1093–1098 (2007)Google Scholar
  9. 9.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) pp. 1–38 (1977)Google Scholar
  10. 10.
    Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M.: Classification of the myoelectric signal using time-frequency based representations. Medical Engineering & Physics 21(6–7), 431–438 (1999)Google Scholar
  11. 11.
    Fukuda, O., Tsuji, T., Kaneko, M., Otsuka, A.: A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Transactions on Robotics and Automation 19 (2003)Google Scholar
  12. 12.
    Gopura, R., Kiguchi, K., Li, Y.: SUEFUL-7: A 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1126–1131 (2009)Google Scholar
  13. 13.
    Khokhar, Z.O., Xiao, Z.G., Menon, C.: Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomedical engineering online 9, 41 (2010)Google Scholar
  14. 14.
    Kiguchi, K., Hayashi, Y.: An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot. IEEE Transactions on Systems, Man, and Cybernetics 42(4), 1064–1071 (2012)Google Scholar
  15. 15.
    Konrad, P.: The abc of EMG. A practical introduction to kinesiological electromyography (April), 1–60 (2005)Google Scholar
  16. 16.
    Lee, S., Sankai, Y.: Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint. In: IEEE/RSJ International Conference on Intelligent Robots and System. vol. 2, pp. 1499–1504 (2002)Google Scholar
  17. 17.
    Lenzi, T., De Rossi, S., Vitiello, N., Carrozza, M.: Intention-based EMG control for powered exoskeletons. IEEE Transactions on Biomedical Engineering 59(8), 2180–2190 (2012)Google Scholar
  18. 18.
    Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. pp. 9–14. IEEE (2010)Google Scholar
  19. 19.
    Lloyd, D.G., Besier, T.F.: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics 36(6), 765–776 (2003)Google Scholar
  20. 20.
    Manal, K., Gravare-Silbernagel, K., Buchanan, T.S.: A Real-time EMG-driven Musculoskeletal Model of the Ankle. Multibody System Dynamics 28(1–2), 169–180 (2012)Google Scholar
  21. 21.
    Michieletto, S., Chessa, N., Menegatti, M.: Learning how to approach industrial robot tasks from natural demonstrations. In: IEEE Workshop on Advanced Robotics and its Social Impacts (2013)Google Scholar
  22. 22.
    Millán, J.d.R., Ferrez, P.W., Galán, F., Lew, E., Chavarriaga, R.: Non-invasive brain-machine interaction. International Journal of Pattern Recognition and Artificial Intelligence 22(05), 959–972 (2008)Google Scholar
  23. 23.
    Millán, J.d.R., Renkens, F., Mouriño, J., Gerstner, W.: Brain-actuated interaction. Artificial Intelligence 159(1–2), 241–259 (2004)Google Scholar
  24. 24.
    Phinyomark, A., Limsakul, C., Phukpattaranont, P.: Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification. Measurement Science Review 11(2), 45–52 (2011)Google Scholar
  25. 25.
    Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using gaussian mixture speaker models. Speech and Audio Processing, IEEE Transactions on 3(1), 72–83 (1995)Google Scholar
  26. 26.
    Romero, J., Feix, T., Kjellstrom, H., Kragic, D.: Spatio-temporal modeling of grasping actions. In: IEEE/RSJ International Conference on Intelligent Robots and System. pp. 2103–2108 (2010)Google Scholar
  27. 27.
    Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G.: EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. PloS one 7(12), e52618 (2012)Google Scholar
  28. 28.
    Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)Google Scholar
  29. 29.
    Suresh, M.: GMM modeling of person information from EMG signals. IEEE Recent Advances in Intelligent Computational Systems pp. 712–717 (2011)Google Scholar
  30. 30.
    Vanacker, G., Millán, J.d.R., Lew, E., Ferrez, P.W., Galán, F., Philips, J., van Brussel, H., Nuttin, M.: Context-based filtering for assisted brain-actuated wheelchair driving. Computational Intelligence and Neuroscience 2007, 1–12 (2007)Google Scholar
  31. 31.
    Wallace, C.S., Dowe, D.L.: Minimum message length and kolmogorov complexity. The Computer Journal 42(4), 270–283 (1999)Google Scholar
  32. 32.
    Wang, L., Buchanan, T.S.: Prediction of joint moments using a neural network model of muscle activations from EMG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10, 30–37 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Stefano Michieletto
    • 1
  • Luca Tonin
    • 1
  • Mauro Antonello
    • 1
  • Roberto Bortoletto
    • 1
  • Fabiola Spolaor
    • 2
  • Enrico Pagello
    • 1
  • Emanuele Menegatti
    • 1
  1. 1.Intelligent Autonomous Systems Lab (IAS-Lab), Department of Information Engineering (DEI)University of PadovaPaduaItaly
  2. 2.Movement Analysis Laboratory, Department of Information Engineering (DEI)University of PadovaPaduaItaly

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