Informatik - Forschung und Entwicklung

, Volume 22, Issue 3, pp 173–183 | Cite as

Auswertung von elektromyographischen Signalen zur Steuerung von Exoskeletten

Reguläre Beiträge

Zusammenfassung

Diese Arbeit stellt ein Modell und ein System zur Steuerung von Exoskeletten mit Hilfe von elektrischen Signalen vor, die an den Muskeln des Benutzers gemessen werden. Anhand dieser Signale wird der eigene Drehmomentbeitrag des Benutzer zur gewünschten Bewegung abgeschätzt, und ein einstellbarer Faktor bestimmt das vom Exoskelett hinzugefügte Drehmoment in Bezug auf den Beitrag des Benutzers. Die Signale werden durch ein komplexes biomechanisches Modell ausgewertet.

Schlagworte

Exoskelett  EMG-Signale  Signalverarbeitung 

Abstract

This paper presents a model and control scheme for actuated exoskeletons by means of electrical signals recorded from muscles of the operator. Those signals are used to estimate the torque contribution of the operator to the desired movement. An adjustable ratio defines the extra torque the exoskeleton should contribute in relation to the torque of the user. The EMG signal evaluation is performed by a sophisticated biomechanical model.

Keywords

Exoskeleton  EMG signals  Signal evaluation 

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Literatur

  1. 1.
    Delsys Inc., http://www.delsys.com, 8.2.2008Google Scholar
  2. 2.
    disynet, http://www.sensoren.de/, 8.2.2008Google Scholar
  3. 3.
    Maxon Motor, http://www.maxonmotor.co.uk/, 8.2.2008Google Scholar
  4. 4.
    RealTime Application Interface (RTAI), https://www.rtai.org/, 8.2.2008Google Scholar
  5. 5.
    Royal Philips Electronics, http://www.philips.com/, 8.2.2008Google Scholar
  6. 6.
    An K, Takahashi K, Harrigan T, Chao E (1984) Determination of muscle orientations and moment arms. J Biomech Eng 106(3):280–2CrossRefGoogle Scholar
  7. 7.
    Basmajian JV, De Luca CJ (1985) Muscles Alive: Their Functions Revealed by Electromyography. Williams&Wilkins, BaltimoreGoogle Scholar
  8. 8.
    Buchanan T, Lloyd D, Manal K, Besier T (2004) Neuromusculoskeletal Modeling: Estimation of Muscle Forces and Joint Moments and Movements From Measurements of Neural Command. J Appl Biomech 20:367–395Google Scholar
  9. 9.
    Delp S, Loan J, Hoy M, Zajac F, Topp E, Rosen J (1990) An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Trans Biomed Eng 37(8):757–767CrossRefGoogle Scholar
  10. 10.
    Fasoli S, Krebs H, Stein J, Frontera W, Hogan N (2003) Effects of robotic therapy on motor impairment and recovery in chronic stroke. Arch Phys Med Rehabil 84(4):477–482CrossRefGoogle Scholar
  11. 11.
    Ferris D (2005) Powered Lower Limb Orthoses for Gait Rehabilitation. Top Spinal Cord Injury Rehabil 11(2):34–49CrossRefGoogle Scholar
  12. 12.
    Ferris D, Czerniecki J, Hannaford B (2005) An Ankle-Foot Orthosis Powered by Artificial Pneumatic Muscles. J Appl Biomech 21(2):189–197Google Scholar
  13. 13.
    Fleischer C, Hommel G (2007) Calibration of an EMG-Based Body Model with six Muscles to control a Leg Exoskeleton. In: IEEE International Conference on Robotics and Automation, pp 2514–2519Google Scholar
  14. 14.
    Hill A (1938) The Heat of Shortening and the Dynamic Constants of Muscle. Proc R Soc London Ser B, Biol Sci 126(843):136–195Google Scholar
  15. 15.
    Hussein S, Granat M (2002) Intention detection using a neuro-fuzzy EMG classifier. IEEE Eng Med Biol Mag 21(6):123–129CrossRefGoogle Scholar
  16. 16.
    Ito K, Tsuji T, Kato A, Ito M (1992) EMG Pattern Classification for a Prosthetic Forearm with Three Degrees of Freedom. Proceeding of IEEE International Workshop on Robot and Human Communication 92 (Tokyo) pp 69–74Google Scholar
  17. 17.
    Kawai S, Yokoi H, Naruse K, Kakazu Y (2004) Study for control of a power assist device. Development of an EMG based controller considering a human model. Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems 3Google Scholar
  18. 18.
    Kawamoto H, Sankai Y (2002) Comfortable power assist control method for walking aid by HAL-3. Systems, Man and Cybernetics, 2002 IEEE International Conference on 4:6Google Scholar
  19. 19.
    Kawamoto H, Sankai Y (2004) Power assist method based on phase sequence driven by interaction between human and robot suit. 13th IEEE International Workshop on Robot and Human Interactive Communication, 2004, pp 491–496Google Scholar
  20. 20.
    Kazerooni H (2005) Exoskeletons for human power augmentation. Intelligent Robots and Systems, 2005(IROS 2005) 2005 IEEE/RSJ International Conference on pp 3459–3464Google Scholar
  21. 21.
    Kazerooni H, Steger R (2006) The Berkeley Lower Extremity Exoskeleton. J Dyn Syst Meas Control 128:14–25CrossRefGoogle Scholar
  22. 22.
    Lee S, Sankai Y (2002) Power assist control for leg with HAL-3 based on virtual torque and impedance adjustment. Systems, Man and Cybernetics, 2002 IEEE International Conference on 4:6Google Scholar
  23. 23.
    Liu X, Low K, Yu H (2004) Development of a lower extremity exoskeleton for human performance enhancement. Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems 4:3889–3894Google Scholar
  24. 24.
    Lunenburger L, Colombo G, Riener R, Dietz V (2005) Clinical Assessments Performed During Robotic Rehabilitation by the Gait Training Robot Lokomat. Rehabilitation Robotics, 2005 ICORR 2005 9th International Conference on pp 345–348Google Scholar
  25. 25.
    Potvin J, Norman R, McGill S (1996) Mechanically corrected EMG for the continuous estimation of erector spinae muscle loading during repetitive lifting. Eur J Appl Physiol Occup Physiol 74:119–132CrossRefGoogle Scholar
  26. 26.
    Pratt J, Krupp B, Morse C, Collins S (2004) The RoboKnee: an exoskeleton for enhancing strength and endurance during walking. Robotics and Automation, 2004 Proceedings ICRA’04 2004 IEEE International Conference on 3:2430–2435Google Scholar
  27. 27.
    Rosen J, Brand M, Fuchs MB, Arcan M (2001) A myosignal-based powered exoskeleton system. In: IEEE Transactions on Systems, Man, and Cybernetics, vol 31Google Scholar
  28. 28.
    Schmidt H, Werner C, Bernhardt R, Hesse S, Krüger J (2007) Gait rehabilitation machines based on programmable footplates. J NeuroEng Rehabil 4:2CrossRefGoogle Scholar
  29. 29.
    Scott S, Winter D (1991) A comparison of three muscle pennation assumptions and their effect on isometric and isotonic force. J Biomech 24(2):163–167CrossRefGoogle Scholar
  30. 30.
    Uhlmann K (1996) Lehrbuch der Anatomie des Bewegungsapparates. UTB für WissenschaftGoogle Scholar
  31. 31.
    Winters J (1990) Hill-based muscle models: a systems engineering perspective. Multiple Muscle Systems: Biomechanics and Movement Organization pp 69–93Google Scholar
  32. 32.
    Zardoshti-Kermani M, Wheeler B, Badie K, Hashemi R (1995) EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans Rehabil Eng, [see also IEEE Trans on Neural Syst Rehabil] 3(4):324–333CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Institut für Technische Informatik und Mikroelektronik (Sekr. EN 10)Technische Universität BerlinBerlinDeutschland

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