Advertisement

Single Channel Surface EMG Based Biometrics

  • Samer Chantaf
  • Lobna Makni
  • Amine Nait-aliEmail author
Chapter
First Online:
  • 252 Downloads
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

An emerging biometric method based on surface EMG (SEMG) signals is considered. For this purpose, this chapter consists of two main parts. The first part reviews the SEMG signals in response to a force of fixed intensity from which frequencial parameters are extracted from the Power Spectral Density (PSD). The second part considers the M-wave signals muscle response following an electrical stimulation. M-wave signals are then characterized by extracting parameters using wavelet networks. The radial basis neural network (RBF) method is then used to classify these parameters.

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

References

  1. 1.
    Marple, S.L.: Digital Spectral Analysis with Applications. Prentice-Hall, Englewood Cliffs, NJ, USA (1987)Google Scholar
  2. 2.
    Chantaf, S., Naït-Ali, A., Karasinski, P., Khalil, M.: ECG modelling using wavelet networks: application to biometrics. Int. J. Biometrics 2(3), 236–249 (2010)CrossRefGoogle Scholar
  3. 3.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back propagation errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  4. 4.
    Poggio, T., Girosi, F.: Networks for approximation and learning. Proc. IEEE 78(9), 1481–1497 (1990)CrossRefGoogle Scholar
  5. 5.
    Chantaf, S.: Biométrie par signaux physiologiques. Ph.D. Thesis. Université Paris Est Créteil (UPEC), Paris, France (2011)Google Scholar
  6. 6.
    Chantaf, S.: Thesis, Biométrie par signaux physiologiques (2011)Google Scholar
  7. 7.
    Krishnamohan, P., Holi, M.S., et al.: Gmm modeling of person information from emg signals. In Proceeding of the 2011 IEEE Recent Advances in Intelligent Computational Systems (RAICS’11), Trivandrum, India, pp. 712–717 (2011)Google Scholar
  8. 8.
    Kim, S.-H., Ryu, J.-H., Lee, B.-H., Kim, D.-H.: Human identification using emg signal based artificial neural network. J. Inst. Electron. Inf. Eng. 53(4), 142–148 (2016)Google Scholar
  9. 9.
    Kim, J.S., Pan, S.B.: A study on EMG-based biometrics. J. Internet Serv. Inf. Secur. 7(2), 19–31 (2017)Google Scholar
  10. 10.
    Yamaba, H., Kurogi, T., Aburada, A., et al.: On applying support vector machines to a user authentication method using surface electromyogram signals. Artif. Life Robot. 22, 1–7 (2017)CrossRefGoogle Scholar
  11. 11.
    Yamaba, H., Kurogi, T., Kubota, S., et al.: An attempt to use a gesture control armband for a user authentication system using surface electromyograms. Int. Symp. Artif. Life Rob. 342–245 (20160Google Scholar
  12. 12.
    Yamaba, H., Kurogi, T., Kubota, S., et al.: Evaluation of feature values of surface electromyograms for user authentication on mobile devices. Artif. Life Robot. 22, 108–112 (2017)CrossRefGoogle Scholar
  13. 13.
    Kurogi, T., Yamaba, H., Aburada, K., Katayama, T., Park, M., Okazaki, N.: A study on a user identification method using dynamic time warping to realize an authentication system by s-EMG. Int. Conf. Emerg. Intell. Data and Web Technol., 889–900 (2018)Google Scholar
  14. 14.
    Kay, S.: Modern Spectral Estimation Theory and Application. Englewood Cliffs, Prentice-Hall, NJ, USA (1988)zbMATHGoogle Scholar
  15. 15.
    Marple, S.L.: Digital Spectral Analysis with Application. Englewood Cliffs, Prentice-Hall, NJ, USA (1987)Google Scholar
  16. 16.
    Proakis, J.G., Manolakis, D.G.: Digital Signal Processing Principles, Algorithms, and Applications. Englewood Cliffs, Prentice-Hall, NJ (1996)Google Scholar
  17. 17.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Macmillan College Publishing Company, New York (1998)Google Scholar
  18. 18.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)CrossRefGoogle Scholar
  19. 19.
    Robinson, A.J., Snyder-Mackler, L.: Clinical electrophysiology: electrotherapy and testing procedures. In: 2007, Chapter 12: Clinical Electrophysiologic Examination and Evaluation, 3rd edn, p. 424, Lippincott Williams and Wilkins, Baltimore, MDGoogle Scholar
  20. 20.
    Yamaba, H., Nagatomo, S., Aburada, K., et al.: An authentication method for mobile devices that is independent of tap-operation on a touchscreen. J. Robot. Netw. Artif. Life 1, 60–63 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Lebanese University (LU)TripoliLebanon
  2. 2.Université Paris-Est, LISSI, UPECVitry sur SeineFrance

Personalised recommendations

Cite chapter

Buy options