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Development of a Surface EMG Acquisition System with Novel Electrodes Configuration and Signal Representation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8102))

Abstract

Surface EMG signal is a quite useful tool for both the clinical application and human-machine interface. This paper proposes a multi-channel sEMG acquisition system with a novel sEMG electrodes array using improved bipolar montage. The proposed array employs elastic fabric to fix 18 dry electrodes, which make it own the advantages of reusability, wearability and flexibility. Moreover, a new graphic presentation of forearm sEMG signal is proposed, from which muscular activities can be observed instinctively in the form of round image patterns. At the end of this paper, several groups of hand gestures are studied to show the potential of the proposed system.

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References

  1. Chan, A.D., MacIsaac, D.: Cleanemg: Assessing the quality of emg signals. In: 34th Conference of the Canadian Medical Biological Engineering Society and Festival of International Conferences on Caregiving, Disability, Aging and Technology, pp. 1–4.

    Google Scholar 

  2. Lapatki, B.G., van Dijk, J.P., Jonas, I.E., Zwarts, M.J., Stegeman, D.F.: A thin, flexible multielectrode grid for high-density surface EMG. Journal of Applied Physiology 96(1), 327–336 (2004)

    Article  Google Scholar 

  3. Zaini, M.H.M., Ahmad, S.A.: Surgical and non-surgical prosthetic hands control: A review, September 25-28, pp. 634–637 (2011)

    Google Scholar 

  4. Hargrove, L., Englehart, K., Hudgins, B.: A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed Signal Process Control 3(2), 175–180 (2008)

    Article  Google Scholar 

  5. Ju, Z.J., Liu, H.H.: A unified fuzzy framework for human-hand motion recognition. IEEE Transactions on Fuzzy Systems 19(5), 901–913 (2011)

    Article  Google Scholar 

  6. Ju, Z., Liu, H.: Recognizing hand grasp and manipulation through empirical copula. International Journal of Social Robotics 2(3), 321–328 (2010)

    Article  Google Scholar 

  7. Tang, X., Liu, Y., Lv, C., Sun, D.: Hand motion classification using a multichannel surface electromyography sensor. Sensors (Basel) 12(2), 1130–1147 (2012)

    Article  Google Scholar 

  8. Oskoei, M.A., Hu, H.S.: Myoelectric control systems-a survey. Biomed Signal Process Control 2(4), 275–294 (2007)

    Article  Google Scholar 

  9. Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 50(7), 848–854 (2003)

    Article  Google Scholar 

  10. Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R.: Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. In: Conference Proceeding of the Chi 2008: 26th Annual Chi Conference on Human Factors in Computing Systems, vol. 1 and 2, pp. 515–524 (2008)

    Google Scholar 

  11. Rojas-Martinez, M., Mananas, M.A., Alonso, J.F.: High-density surface emg maps from upper-arm and forearm muscles. J. Neuroeng. Rehabil. 9, 85 (2012)

    Article  Google Scholar 

  12. Al-Timemy, A.H., Bugmann, G., Escudero, J., Outram, N.: Classication of Finger movements for the dexterous hand prosthesis control with surface electromyography (2013)

    Google Scholar 

  13. Tenore, F.V.G., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., Thakor, N.V.: Decoding of individuated finger movements using surface electromyography. IEEE Transactions on Biomedical Engineering 56(5), 1427–1434 (2009)

    Article  Google Scholar 

  14. Du, Y.C., Lin, C.H., Shyu, L.Y., Chen, T.S.: Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis. Expert Systems with Applications 37(6), 4283–4291 (2010)

    Article  Google Scholar 

  15. Kendell, C., Lemaire, E.D., Losier, Y., Wilson, A., Chan, A., Hudgins, B.: A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics. J. Neuroeng. Rehabil. 9, 24 (2012)

    Article  Google Scholar 

  16. Day, S.: Important factors in surface emg measurement, pp. 1–17. Bortec Biomedical Ltd publishers (2002)

    Google Scholar 

  17. Staudenmann, D., Kingma, I., Stegeman, D.F., van Dieen, J.H.: Towards optimal multi-channel emg electrode configurations in muscle force estimation: a high density emg study. J. Electromyogr Kinesiol 15(1), 1–11 (2005)

    Article  Google Scholar 

  18. Malboubi, M., Razzazi, F., Aliyari, S.M.: Elimination of power line noise from emg signals using an efficient adaptive laguerre filter. In: 2010 International Conference on Signals and Electronic Systems (ICSES), pp. 49–52 (2010)

    Google Scholar 

  19. De Luca, C.J., Gilmore, L.D., Kuznetsov, M., Roy, S.H.: Filtering the surface emg signal: Movement artifact and baseline noise contamination. J. Biomech. 43(8), 1573–1579 (2010)

    Article  Google Scholar 

  20. Momen, K., Krishnan, S., Chau, T.: Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15(4), 535–542 (2007)

    Article  Google Scholar 

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Fang, Y., Zhu, X., Liu, H. (2013). Development of a Surface EMG Acquisition System with Novel Electrodes Configuration and Signal Representation. In: Lee, J., Lee, M.C., Liu, H., Ryu, JH. (eds) Intelligent Robotics and Applications. ICIRA 2013. Lecture Notes in Computer Science(), vol 8102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40852-6_41

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  • DOI: https://doi.org/10.1007/978-3-642-40852-6_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40851-9

  • Online ISBN: 978-3-642-40852-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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