Study on Real-Time Control of Exoskeleton Knee Using Electromyographic Signal (original) (raw)

Abstract

This paper is concerned with control method for exoskeleton in real-time by using electromyographic signal (EMGs). EMGs is collected from normal subjects when they move their knee flexion-extension in the sagittal plane. The raw EMGs is processed and then input to a four-layer feed-forward neural network model which uses the back-propagation training algorithm. The output signal from neural network is processed by the wavelet transform. Finally, the control orders are passed to the motor controller and drive the exoskeleton knee move by the same way. In this study, the correlation coefficient is used to evaluate the effects of neural network prediction. The experimental results show that the proposed method can accurately control the movement of the knee joint.

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References

  1. Pullman, S.L., Goodin, D.S., Marquinez, A.I.: Clinical utility of surface EMG. J. Neurology 55, 71–177 (2000)
    Google Scholar
  2. Hakkinen, K., Kallinen, M., Izquierdo, M.: Changes in agonist-antagonist EMG, muscle CSA, and force during strength training in middle-aged and older people. J. Appl. Physiol. 84, 84–1341 (1998)
    Google Scholar
  3. Magnusson, S.P., Simonsen, E.B., Dyhre-Poulsen, P.: Viscoelastic stress relaxation during static stretch in human skeletal muscle in the absence of EMG activity. J. Med. Sci. Sports. 6, 323–328 (1996)
    Article Google Scholar
  4. Artemiadis, P.K., Kyriakopoulos, K.J.: EMG-based Teleoperation of a Robot Arm in Planar Catching Movements using ARMAX Model and Trajectory Monitoring Techniques. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 489–495 (2007)
    Google Scholar
  5. Burridge, J., Freeman, C., Hughes, A.M.: Relationship between changes in tracking per-formance and timing and amplitude of biceps and triceps EMG following training in a planar arm robot in a sample of people with post-stroke hemiplegia. In: The XVIII Congress of the International Society of Electrophysiology and Kinesiology, Aalborg, Denmark, June 16-19
    Google Scholar
  6. Fleischer, C., Wege, A., Kondak, K.: Application of EMG signals for controlling exoskeleton robots. J. Biomedizinische Technik 51, 314–319 (2006)
    Article Google Scholar
  7. Lloyd, D.G., Besier, T.F.: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J. Biomechanics 36, 765–776 (2003)
    Article Google Scholar
  8. Zhang, Z., Yao, S.L., Zhang, Y.N.: On the surface electromyography sensor network of human ankle movement. In: IEEE International Conference on Robotics and Biomimetics, Shanghai, pp. 1688–1692 (2007)
    Google Scholar
  9. Yao, S.L., Zhang, Y.N., Zhang, Z.: Controlling Ankle Movement in Neuro-rehabilitation Using Selective Surface Electromyography Signals. J. Journal of Shanghai University 15 (2009)
    Google Scholar
  10. Hiraiwa, A., Shimohara, K., Tokunaga, Y.: EMG pattern analysis and classification by neural network. In: Proc. IEEE Int. Conf. Systems, pp. 1113–1115 (1989)
    Google Scholar
  11. Li, X.F., Yang, J.J., Shi, Y.: Motion state identification of human elbow joint based on EMGs. J. Chinese Journal of Biomedical Engineering 24 (2005)
    Google Scholar
  12. Kawainot, H., Siiwoong Lee, O., Kanbe, S.: Power Assist Method for HAL-3 using EMG-based Feedback Controller. In: Proceedings of the IEEE International Conference on Systems, Washington, pp. 1648—1623 (2003)
    Google Scholar
  13. Shrirao, N.A., Reddy, N.P., Kosuri, D.R.: Neural network committees for finger joint angle estimation from surface EMG signals. J. BioMedical Engineering OnLine
    Google Scholar

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Authors and Affiliations

  1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 20072, P.R. China
    Jiaxin Jiang, Zhen Zhang, Zhen Wang & Jinwu Qian

Authors

  1. Jiaxin Jiang
  2. Zhen Zhang
  3. Zhen Wang
  4. Jinwu Qian

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Editors and Affiliations

  1. Electrical Engineering and Computer Science, The Queen’s University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK
    Kang Li
  2. School of Mechatronical Engineering and Automation, Shanghai University, P.O.Box 183, 200072, Shanghai, China
    Li Jia , Xin Sun & Minrui Fei , &
  3. School of Electronics, Electrical Engineering and Computer Science, The Queen’s University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK
    George W. Irwin

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© 2010 Springer-Verlag Berlin Heidelberg

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Jiang, J., Zhang, Z., Wang, Z., Qian, J. (2010). Study on Real-Time Control of Exoskeleton Knee Using Electromyographic Signal. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1\_10

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