Advertisement

Arm Orthosis/Prosthesis Control Based on Surface EMG Signal Extraction

  • Aaron Suberbiola
  • Ekaitz Zulueta
  • Jose Manuel Lopez-Guede
  • Ismael Etxeberria-Agiriano
  • Bren Van Caesbroeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

The goal of this paper is to show EMG based system control applied to motorized orthoses. Through two biometrical sensors it captures biceps and triceps EMG signals, which are then filtered and processed by an acquisition system. Finally an output/control signal is produced and sent to the actuators, which will then perform the proper movement. The research goal is to predict the movement of the lower arm through the analysis of EMG signals, so that the movement can be reproduced by an arm orthosis, powered by two linear actuators.

Keywords

Orthosis Prosthesis Control EMG Power assistance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kawamoto, H., Sankai, Y.: Power assist system HAL-3 for gait disorder person. In: Miesenberger, K., Klaus, J., Zagler, W.L. (eds.) ICCHP 2002. LNCS, vol. 2398, pp. 196–203. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Kawamoto, H., Kanbe, S., Sankai, Y.: Power assist method for HAL-3 estimating operator’s intention based on motion information. In: IEEE Workshop on Robot and Human Interactive Communiaction (Millbrae), pp. 67–72 (2003)Google Scholar
  3. 3.
    Kawamoto, H., Suwoong, L., Kanbe, S., Sankai, Y.: Power assist method for HAL-3 using EMG-based feedback controller. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1648–1653 (2003)Google Scholar
  4. 4.
    Yamamoto, K., Hyodo, K., Ishii, M., Matsuo, T.: Development of power assisting suit for assisting nurse labor. JSME International Journal Series, 703–711 (2002)Google Scholar
  5. 5.
    Yamamoto, K., Hyodo, K., Ishii, M., Yoshimitsu, T., Matsuo, T.: Development of power assisting suit. JSME International Journal Series, 923–930 (2003)Google Scholar
  6. 6.
    Pratt, J.E., Krupp, B.T., Morse, C.J., Collins, S.H.: The RoboKnee: An exoskeleton for Enhancing Strength and Endurance During Walking. In: IEEE International Conference on Robotics and Automation (New Orleans), pp. 2430–2435 (2004)Google Scholar
  7. 7.
    Kong, K., Jeon, D.: Design and control of an exoskeleton for the elderly and patients. IEEE/ASME Transactions on Mechatronics, 220–226 (2006)Google Scholar
  8. 8.
    Agrawal, S.K., Fattah, A.: Theory and design of an orthotic device for full or partial gravity-balancing of a human leg during motion. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 157–165 (2004)Google Scholar
  9. 9.
    Day, S.: Important factors in surface EMG measurement. Bortec (2009) Google Scholar
  10. 10.
    Reaz, M.B.I., Hussain, M.S., Mohd-Yasin, F.: Techniques of EMG analysis: detection, processing, classification and applications. Biological Procedures (2006)Google Scholar
  11. 11.
    Hug, F.: Can muscle coordination be precisely studied by surface electromyography? Journal of Electromyography and Kinesiology 21, 1–12 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hermens, H.J., Freriks, B., Disselhorst-Klug, C., Rau, G.: Development of recommendations for SEMG sensor placement procedures. Journal of Electromyography and Kinesiology 10, 367–374 (2000)CrossRefGoogle Scholar
  13. 13.
    Ng, A.Y.: Lecture on machine learning: principal component analysis and independent component analysis in relation to unsupervised machine learning, Stanford (2008) Google Scholar
  14. 14.
    Havran, C., Hupet, L., Czyz, J., Lee, J., Vandendorpe, L., Verleysem, M.: Independent component analysis for face authentication. In: Knowledge-based Intelligent Information Engineering Systems & Allied Technologies. IOS Press, Crema (2009) Google Scholar
  15. 15.
    Agrawal, A.: Independent component analysis vs factor analysis. ENEE698A Seminar (2003) Google Scholar
  16. 16.
    Ripley, B.: Principal component analysis and factor analysis. University of Oxford: Department of Statics (2009) Google Scholar
  17. 17.
    Hill, T., Lewicki, P.: Statistics: methods and applications. A comprehensive reference for science, industry and data mining. Statsoft (2006) Google Scholar
  18. 18.
    di Milano, P.: A tutorial on clustering algorithms. Home Polimi (2009) Google Scholar
  19. 19.
    Cohn, D.: Mixtures of Gaussians. School of Computer Science Carnegie Mellon University (1996) Google Scholar
  20. 20.
    Moore, A.W.: Clustering with Gaussian Mixtures. School of Computer Sciencie. Carnegie Mellon University (2004) Google Scholar
  21. 21.
    Orjuela, A., Calôba, L.: Clasificación de Movimientos en Extremidades Usando Redes Neuronales: I. Proceso Supervisado. In: 21º Congresso Brasileiro em Engenharia Biomédicas (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aaron Suberbiola
    • 1
  • Ekaitz Zulueta
    • 1
  • Jose Manuel Lopez-Guede
    • 1
  • Ismael Etxeberria-Agiriano
    • 1
  • Bren Van Caesbroeck
    • 1
  1. 1.University of the Basque Country (UPV/EHU)VitoriaSpain

Personalised recommendations