Skip to main content
SpringerLink
Log in
Book cover

International Conference in Swarm Intelligence

ICSI 2015: Advances in Swarm and Computational Intelligence pp 180–188Cite as

Achievement of a Myoelectric Clamp Provided by an Optical Shifting Control for Upper Limb Amputations

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9141))

Abstract

The prehension is a complex biomechanical process; it involves nearly 200 muscles and a large number of joint and bones. Replicate this process is a complex and challenging technology. Nevertheless, it is possible to target apart of this process in order to develop systems to provide a degree of autonomy to people with handicap linked to an amputation of the hand, and the muscles of the forearm are still functional. This paper is about the design of a prototype myoelectric clamp, which can grasp and hold a wide range of usual objects. So the system is activated by EMG stimulations and relays on force and slip feedback to achieve the grasping task. In this work we show the feasibility of a low cost and efficient clamp that can maintain objects by avoiding accidental falls or damages caused by unregulated force applied over the grasped item.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Childress, D., Strysik, J.: Myoelectrically Controlled Artificial Hand. Patent US 4623354 (1986)

    Google Scholar 

  2. Barkhordar, M., Nightingale, J.M., May, D.R.W.: Artificial Hands. Patent US 4650492 (1987)

    Google Scholar 

  3. TouchBionics Active Prosthesis. http://www.touchbionics.com

  4. Kuiken, T.: Consideration of Nerve-Muscle Grafts to Improve the Control of Artificial Arms. Technology & Disability 15, 105–111 (2003)

    Google Scholar 

  5. Miller, L., Lipschutz, R., Stubblefield, K., Lock, B.A., Huang, H., Williams, T., Weir, R., Kuiken, T.: Control of a Six Degree of Freedom Prosthetic Arm After Targeted Muscle Reinnervation Surgery. Archives of Physical Medicine and Rehabilitation 89, 2057–2065 (2008)

    Article  Google Scholar 

  6. Kuiken, T., Dumanian, G., Lipschutz, R., Miller, L., Stubblefield, K.: The Use of Targeted Muscle Reinnervation for Improved Myoelectric Prosthesis Control in a Bilateral Shoulder Disarticulation Amputee. Prosthetics and Orthotics International 28, 245–253 (2004)

    Google Scholar 

  7. Kuiken, T., Li, G., Lock, B.A., Lipschutz, R., Miller, L., Stubblefield, K., Englehart, K.: Targeted Muscle Reinnervation for Real-Time Myoelectric Control of Multifunction Artificial Arms. The Journal of the American Medical Association 301, 619–628 (2009)

    Article  Google Scholar 

  8. Zhou, P., Kuiken, T.: Eliminating Cardiac Contamination from Myoelectric Control Signals Developed by Targeted Muscle Reinnervation. Physiological Measurement 27, 1311 (2006)

    Article  Google Scholar 

  9. Cipriani, C., Zaccone, F., Micera, S., Carrozza, M.C.: On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User-Prosthesis Interaction. IEEE Transaction on Robotics 24, 170–184 (2008)

    Article  Google Scholar 

  10. Johansson, R.S., Landstrom, U., Londstrom, R.: Responses of Mechanoreceptive Afferent Units in the Glabrous Skin of the Human Hand to Sinusoidal Skin Displacements. Experimental Brain Research 244, 17–25 (1982)

    Article  Google Scholar 

  11. Johansson, R.S., Westling, G.: Signals in Tactile Afferents From the Fingers Eliciting Adaptive Motor Responses During Precision Grip. Experimental Brain Research 66, 141–154 (1987)

    Article  Google Scholar 

  12. Johansson, R.S., Westling, G.: Responses in Glabrous Skin Mechanoreceptors During Precision Grip in Humans. Experimental Brain Research 66, 128–140 (1987)

    Article  Google Scholar 

  13. Raspopovic, S., Capogrosso, M., Petrini, F.M., Bonizzato, M., Rigosa, J., Pino, J.D., Carpaneto, J., Controzzi, M., Boretius, T., Fernandez, E., Granata, G., Oddo, C.M., Citi, L., Ciancio, L.A., Cipriani, C., Carrozza, M.C., Jensen, E., Guglielmelli, T., Stieglitz, P.M., Rossini, S.: Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prosthsis. SciTransl. Med. 6, 222 (2014)

    Google Scholar 

  14. Shannon, G.F.: A Comparison of Alternative Means of Providing Sensory Feedback on Upper Limb Prosthesis. Medical and Biological Engineering 14, 284–294 (1976)

    Article  Google Scholar 

  15. Kaczmarek, K.A., Webster, J.G., Rita, P.B.Y., Tompkins, W.G.: Electrotactile and Vibrotactile Displays for Sensory Substitution Systems. IEEE Transactions on Biomedical Engineering 38, 1–16 (1991)

    Article  Google Scholar 

  16. Pylatiuk, C., Kargov, A., Schulz, S.: Design and Evaluation of a Low-Cost Force Feedback System for Myoelectric Prosthetic Hands. American Academy of Orthotists and Prosthetists 18, 5–61 (2006)

    Google Scholar 

  17. Zipp, P.: Effect of Electrode Geometry on the Selectivity of Myoelectric Recordings With Surface Electrodes. European Journal of Applied Physiology and Occupational Physiology 50, 35–40 (1986)

    Article  Google Scholar 

  18. HermensH, J., Freriks, B., Disselhorst-Klug, C., Rau, G.: Development of Recommendations for SEMG Sensors and Sensor Placement Procedures. Journal of Electromypgraphy and Kinesiology 10, 361–374 (2000)

    Article  Google Scholar 

  19. Roy, S., De Luca, G., Cheng, M., Johansson, A., Gilmore, L., De Luca, J.: Electro-Mechanical Stability of Surface EMG Sensors. Med. Bio. Eng. Comput. 45, 447–457 (2007)

    Article  Google Scholar 

  20. Ajiboye, A.B., Weir, R.: A Heuristic Fuzzy Logic Approach to EMG Pattern Recognition for Multifunctional Prosthesis Control. IEEE Neural Systems and Rehabilitation 13, 280–291 (2005)

    Article  Google Scholar 

  21. Hargrove, L., Zhou, P., Englehart, K., Kuiken, T.: The Effect of ECG Interference on Pattern Recognition Based Myoelectric Control for Targeted Muscle Reinnervated Patients. IEEE Trans. Biomed. Eng. 56, 2197–2201 (2009)

    Article  Google Scholar 

  22. Frank Netter, H.: Atlas d’Anatomie Humaine. MASSON, 4th edn., Translation of Pierre Kamina (2007)

    Google Scholar 

  23. Williams, M., Kirsch, R.: Evaluation of Head Orientation and Neck Muscle EMG Signals as Command Inputs to a Human-Computer Interface for Individuals With High Tetraplegia. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 48–496 (2008)

    Article  Google Scholar 

  24. Zipp, P.: Effect of Electrode Geometry on the Selectivity of Myoelectric Recordings With Surface Electrodes. European Journal of Applied Physiology and Occupational Physiology 50, 35–40 (1986)

    Article  Google Scholar 

  25. Chu, J., Moon, I., Mun, M.: A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand. IEEE Biomedical Eng. 53, 2232–2239 (2006)

    Article  Google Scholar 

  26. Pan, T., Fan, L., Chiang, H., Chang, S., Jiang, J.: Mechatronic Experiments Course Design: A Myoelectric Controlled Partial Hand Prosthesis project. IEEE Transactions on Education 47, 348–355 (2004)

    Article  Google Scholar 

  27. Bolek, E.J.: Electrical Concepts in the Surface Electromyographic Signal. Applied Psychophysiology and Biofeedback 35, 171–175 (2009)

    Article  Google Scholar 

  28. Wirta, R., Taylor, D., Fonley, R.: Pattern Recognition Arm Prosthesis: A Historical Perspective-A Final Report. Bulletin of Prosthetics Research 10, 8–35 (1978)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electronic and Computing Faculty (FEI), Robotics Parallelism and Embeded Systems (LRPE), University of Science and Technology Houari Boumediene (USTHB), Algiers, Algeria

    Sofiane Ibrahim Benchabane & Nadia Saadia

Authors
  1. Sofiane Ibrahim Benchabane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadia Saadia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sofiane Ibrahim Benchabane .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'ian Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Universidade de Pernambuco, Recife, Brazil

    Fernando Buarque

  4. Instituto Politécnico Nacional, Mexico, Mexico

    Alexander Gelbukh

  5. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  6. University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Benchabane, S.I., Saadia, N. (2015). Achievement of a Myoelectric Clamp Provided by an Optical Shifting Control for Upper Limb Amputations. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20472-7_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20471-0

  • Online ISBN: 978-3-319-20472-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation