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Finger Movements Classification for the Dexterous Control of Upper Limb Prosthesis Using EMG Signals

  • Ali Al-Timemy
  • Guido Bugmann
  • Nicholas Outram
  • Javier Escudero
  • Hai Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7429)

Abstract

Nowadays, there are thousands of disabled people around the world who had lost a limb. The majority of them are hand amputees with different level of amputation ranging from elbow disarticulation to upper digits amputation[1]. To bring those people back to normal life, amputees used artificial hand prosthesis controlled by the muscle signal known as Surface Electromyography (sEMG) recorded form the skin surface of residual limb of the amputee. The muscle signal is also commonly named as myoelectric signal. These devices will help amputees to improve their lives and make them self-confident.

It has been reported that EMG activity recorded from the amputee forearm muscles after hand amputation are similar to EMG of healthy subjects [2, 3]. Therefore, there is still an EMG signal when the amputee intends to perform a movement. This fact has inspired researchers to develop EMG signal processing algorithms for the control of a prosthetic hand with the electrical signal of the muscles.

Keywords

Finger Movement Residual Limb Myoelectric Signal Finger Flexion Prosthetic Hand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Database, N.A.S.: The Amputee Statistical Database. United KingdomISD Publications, Edinburgh (2009)Google Scholar
  2. 2.
    Su, Y., Wolczowski, A., Fisher, M.H., Bell, G.D., Burn, D., Gao, R.: Towards an EMG controlled prosthetic hand using a 3D electromagnetic positioning system. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference, IMTC, vol. 1 (2005)Google Scholar
  3. 3.
    Zecca, M., Micera, S., Carrozza, M.C., Dario, P.: Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering 30, 459 (2002)CrossRefGoogle Scholar
  4. 4.
    Oskoei, M.A., Hu, H.: Myoelectric control systems–A survey. Biomedical Signal Processing and Control 2, 275–294 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ali Al-Timemy
    • 1
  • Guido Bugmann
    • 1
  • Nicholas Outram
    • 1
  • Javier Escudero
    • 1
  • Hai Li
    • 1
  1. 1.School of Computing and MathematicsPlymouth UniversityPlymouthUK

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