Wearable Multi-channel EMG Biometrics: Concepts

  • Ikram Brahim
  • Islame Dhibou
  • Lobna Makni
  • Sherif Said
  • Amine Nait-aliEmail author
Part of the Series in BioEngineering book series (SERBIOENG)


In this chapter, a case study using a specific wearable Multi-Channel EMG device will be considered. In particular, eight EMG channels will be used through Myo Armband system. The purpose is to deploy a verification biometric system using EMG signals corresponding to hand gestures. More specifically, the idea behind this concept is the capacity to generate a digital signature for each specific hand gesture.


  1. 1.
  2. 2.
    Chowdhury, R.H., Reaz, M.B., Ali, M.A., Bakar, A.A., Chellappan, K., Chang, T.G.: Surface electromyography signal processing and classification techniques. Sensors (Basel) 13, 12431–12466 (2013)CrossRefGoogle Scholar
  3. 3.
    Zecca, M., Micera, S., Carrozza, M.C., Dario, P.: Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit. Rev. Biomed. Eng. 30, 459–485 (2002)CrossRefGoogle Scholar
  4. 4.
    Zhang, X., Zhou, P.: High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans. Biomed. Eng. 59, 1649–1657 (2012)CrossRefGoogle Scholar
  5. 5.
    Liu, J., Zhou, P.: A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury. IEEE Trans. Neural Syst. Rehabil. Eng. 96–103 (2013)CrossRefGoogle Scholar
  6. 6.
    Atzori, M., Müller, H.: Control capabilities of myoelectric robotic prostheses by hand amputees: a scientific research and market overview. Front. Syst. Neurosci. 9, 162 (2015)CrossRefGoogle Scholar
  7. 7.
    Ma, J., Thakor, N.V., Matsuno, F.: Hand and wrist movement control of myoelectric prosthesis based on synergy. IEEE Trans. Hum. Mach. Syst. 45, 74–83 (2015)CrossRefGoogle Scholar
  8. 8.
    Rasouli, M. Ghosh, R., Lee, W.W., Thakor, N.V., Kukreja, S.: Stable force-myographic control of a prosthetic hand using incremental learning. In: Proceedings of the 37th Annual International Conference IEEE Engineering in Medicine Biology And Society, IEEE (2015), pp. 4828–4831 (2015)Google Scholar
  9. 9.
    Jiang, H., Duerstock, B.S., Wachs, J.P.: A machine vision-based gestural interface for people with upper extremity physical impairments. IEEE Trans. Syst. Man Cybern. Syst. 44, 630–641 (2014)CrossRefGoogle Scholar
  10. 10.
    Riillo, F., Quitadamo, L.R., Cavrini, F., Gruppioni, E., Pinto, C.A., Pastò, N.C., Sbernini, L., Albero, L., Saggio, G.: Optimization of EMG-based hand gesture recognition: supervised versus unsupervised data preprocessing on healthy subjects and transradial amputees. Biomed. Signal Process. Control 14, 117–125 (2014)CrossRefGoogle Scholar
  11. 11.
    Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O.: EMG signal classification for human computer interaction: a review. Eur. J. Sci. Res. 33(3), 480–501 (2009)Google Scholar
  12. 12.
    Kim, K.S., Choi, H.H., Moon, C.S., Mun, C.W.: Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr. Appl. Phys. 11(3), 740–745 (2011)CrossRefGoogle Scholar
  13. 13.
    Zhang, H., Zhao, Y., Yao, F., Xu, L., Shang, P., Li, G.: An adaptation strategy of using LDA classifier for EMG pattern recognition. In: Proceedings of the Annual International Conference IEEE Engineering in Medicine Biology and Society, IEEE, pp. 4267–4270 (2013)Google Scholar
  14. 14.
    Amsuss, S., Goebel, P.M., Jiang, N., Graimann, B., Paredes, L., Farina, D.: Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control. IEEE Trans. Biomed. Eng. 61, 1167–1176 (2014)CrossRefGoogle Scholar
  15. 15.
    Jiang, X., Merhi, L.-K., Xiao, Z.G., Menon, C.: Exploration of force MyoGraphy and surface electromyography in hand gesture classification. Med. Eng. Phys. 41, 63–73 (2017)CrossRefGoogle Scholar
  16. 16.
    Tavakoli, M., Benussi, C., Lopes, P., Osorio, A.: Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier. Biomed. Signal Process. Control 46, 121–130 (2018)CrossRefGoogle Scholar
  17. 17.
    Yamaba, H., Kurogi, T., Aburada, A., Kubota, S., Katayama, T., Park, M.: Naonobu Okazaki: on applying support vector machines to a user authentication method using surface electromyogram signals. Artif. Life Robot. 23(1), 87–93 (2018)CrossRefGoogle Scholar
  18. 18.
    Zhang, D., Zhao, X., Han, J., Zhao, Y.: A comparative study on PCA and LDA based EMG pattern recognition for anthropomorphic robotic hand. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4850–4855 (2014)Google Scholar
  19. 19.
    Adewuyi, A.A., Hargrove, L.J., Kuiken, T.A.: Evaluating EMG feature and classifier selection for application to partial-hand prosthesis control. Front. Neurorobotics 10 (2016)Google Scholar
  20. 20.
    Too, J., Abdullah, A.R., Zawawi, T.N., Saad, N.M., Musa, H.: Classification of EMG signal based on time domain and frequency domain features. Int. J. Hum. Technol. Interact. 1, 25–29 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ikram Brahim
    • 1
  • Islame Dhibou
    • 1
  • Lobna Makni
    • 1
  • Sherif Said
    • 1
    • 2
  • Amine Nait-ali
    • 1
    Email author
  1. 1.Université Paris-Est, LISSI, UPECVitry sur SeineFrance
  2. 2.American University of Middle-East (AUM)EgailaKuwait

Personalised recommendations