Signal Condition and Acquisition System for a Low Cost EMG Based Prosthetic Hand

  • B. Koushik
  • J. Roopa
  • M. Govinda Raju
  • Biswajith Roy
  • H. Manohar
  • K. S. Geetha
  • B. S. Satyanarayana
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

The rehabilitation process for the physically challenged people is a real challenge and concern where technology has not yet reached all sections of the society. This paper presents a novel and a low cost multi finger Robotic prosthetic hand based on the principle of Electromyography (EMG). A three channel EMG sensor is placed across the forearm to detect the voluntary actions of the superficial muscles in the forearm. The low frequency interference and noise in the signal was removed using sensor module that eliminates the need for high end processor and bringing down the cost. The unique nature of this prototype is that, it is adaptable to different people just by adjusting a potentiometer in the sensor module. The proposed prototype of the robotic prosthetic hand is a low cost, non-invasive, painless solution for prosthesis which is capable of performing some basic actions.

Keywords

Electromyography (EMG) Robotic prosthetics Rehabilitation Myoelectric prosthetics 

References

  1. 1.
    Jun-Furukawa, Tomoyuki Noda.: Estimating Joint Movements from Observed EMG signals with multiple electrodes under Sensor Failure Situations towards safe assistive robot control. IEEE International Conference on Robotics and Automation(ICRA), Washington State Convention Centre (2015).Google Scholar
  2. 2.
    T. R. Farrell and R. F. Weir.: The optimal controller delay for myoelectric prostheses. IEEE Trans. Neural Syst. Rehabil. Eng., vol. 15, no. 1, pp. 111–118 (2007).Google Scholar
  3. 3.
    D. Wang, K.M. Lee, J. Guo, and C. J. Jang.: Adaptive knee joint exoskeleton based on biological geometries. IEEE/ASME Trans. Mechatronics, vol. 19, no. 4, pp. 1268–1278 (2014).Google Scholar
  4. 4.
    P. Geethanjali and K.K. Ray.: A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand. IEEE/ASME Trans. Mechatronics, vol. 20, no. 4, pp. 1948–1955 (2015).Google Scholar
  5. 5.
    Carlo J. De Luca.: Surface Electromyography: Detection and Recording. DelSys Incorporated, (2002).Google Scholar
  6. 6.
    Pradeep Shenoy, Kai J. Miller, Beau Crawford, and Rajesh P. N. Rao.: Online Electromyographic Control of a Robotic Prosthesis. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 55, NO. 3, (2008).Google Scholar
  7. 7.
    L. Eriksson, F. Sebelius, and C. Balkenius: Neural control of a virtual prosthesis. presented at the ICANN, Skoevde, Sweden, (1998).Google Scholar
  8. 8.
    Andrea Merlo and Isabella Campanini.: Technical Aspects of Surface Electromyography for Clinicians. The Open Rehabilitation Journal, 3, 98–109 (2010).Google Scholar
  9. 9.
    C.Pylatiuk, M. Müller-Riederer, A. Kargov, S. Schulz, O. Schill, M. Reischl and G. Bretthauer.: Comparison of Surface EMG Monitoring Electrodes for Long-term Use in Rehabilitation Device Control. IEEE 11th International Conference on Rehabilitation Robotics, Kyoto International Conference Center, Japan (2009).Google Scholar
  10. 10.
    Ruchika, Shalini Dhingra.: An Explanatory Study of the Parameters to Be Measured from EMG Signal. International Journal of Engineering and Computer Science ISSN: 2319–7242 Volume 2 Issue 1, Page No. 207–213 (2014).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • B. Koushik
    • 1
  • J. Roopa
    • 1
  • M. Govinda Raju
    • 1
  • Biswajith Roy
    • 1
  • H. Manohar
    • 1
  • K. S. Geetha
    • 1
  • B. S. Satyanarayana
    • 2
  1. 1.Department of Electronics and CommunicationRV College of EngineeringBengaluruIndia
  2. 2.Munjal UniversityGurugaonIndia

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