Skip to main content
SpringerLink
Log in

Recognition from EMG Signals by an Evolutional Method and Non-negative Matrix Factorization

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2773))

Abstract

In this paper, we propose a method of the noise rejection from a signal acquired from many channels using the Electromyograph (EMG) signals. The EMG signals is acquired by the 4th electrodes. EMG signals of 4ch(es) is decomposed into two processions using Non-Negative Matrix Factorization(NMF). And noise rejection is performed by applying the filter obtained by GA to the decomposed matrix . After performing noise rejection, EGM signals is reconstructed and the acquired EMG signal is recognized. The EMG signals based on 7 operations at a wrist are measured. We show the effectiveness of this method by means of computer simulations.

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

Access this chapter

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 74.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.00
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Nishikawa, D., Yu, W., Yokoi, H., Kakazu, Y.: n-Line Learning Method for EMG Prosthetic Hand Controlling. IEICE, D-II J82(9), 1510–1519 (1999) (in Japanese)

    Google Scholar 

  2. Nishikawa, D., Yokoi, H., Kakazu, Y.: Design of motion-recognizer using electoromyogram. In: Robotics and Mechatronics Division (June 1998) (in Japanese)

    Google Scholar 

  3. Nishikawa, D., Yu, W., Yokoi, H., Kakazu, Y.: On-Line Supervising Mechanism for Learning Data in Surface Electromyogram Motion Classifiers. IEICE, D-II J84(12), 2634–2643 (2001)

    Google Scholar 

  4. Kawamura, Y., Nozawa, M., Imai, H., Sankai, Y.: Stand-up Motion Control for Humanoid using Human’s Motion Pattern and EMG. Academic lecture in the 19th Robotics Society of Japan (2001)

    Google Scholar 

  5. Vuskovic, M., Du., S.: Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks. JIJCNN, Honolulu, Hawaii (May 2002)

    Google Scholar 

  6. Lee, D., Seung, H.: Algorithms for non-negative matrix factorization. In: NIPS 2000 (2000)

    Google Scholar 

  7. Lee, D., Seung, H.: Learing the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)

    Article  Google Scholar 

  8. Omachi, S., Yokoyama, H., Aso, H.: Subject Arrangement in the Euclidean Space Using Algorithm. IEICE, D-II J82-D-II(12), 2195–2202 (1999) (in Japanese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Tokushima, 2-1 Minami-josanjima, Tokushima, 770-8506, Japan

    Yuuki Yazama, Minoru Fukumi & Norio Akamatsu

  2. Okayama University, 3-1-1 Tsushima, Okayama, Japan

    Yasue Mitsukura

Authors
  1. Yuuki Yazama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasue Mitsukura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minoru Fukumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Norio Akamatsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, United Kingdom

    Vasile Palade

  2. Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, BN2 4GJ, Brighton, United Kingdom

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, 5095, Adelaide, SA, Australia

    Lakhmi Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yazama, Y., Mitsukura, Y., Fukumi, M., Akamatsu, N. (2003). Recognition from EMG Signals by an Evolutional Method and Non-negative Matrix Factorization. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_81

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45224-9_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40803-1

  • Online ISBN: 978-3-540-45224-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation