A Study on the Pattern Recognition of EMG Signals for Head Motion Recognition

머리 움직임 인식을 위한 근전도 신호의 패턴 인식 기법에 관한 연구

  • 이태우 (서울시립대학교 전자전기 컴퓨터 공학부) ;
  • 전창익 (서울시립대학교 전자전기 컴퓨터 공학부) ;
  • 이영석 (청운대학교 전자공학과) ;
  • 유세근 (청운대학교 전자공학과) ;
  • 김성환 (서울시립대학교 전자전기 컴퓨터 공학부)
  • Published : 2004.02.01

Abstract

This paper proposes a new method on the EMG AR(autoregressive) modeling in pattern recognition for various head motions. The proper electrode placement in applying AR or cepstral coefficients for EMG signature discrimination is investigated. EMG signals are measured for different 10 motions with two electrode arrangements simultaneously. Electrode pairs are located separately on dominant muscles(S-type arrangement), because the bandwidth of signals obtained from S-type placement is wider than that from C-type(closely in the region between muscles). From the result of EMG pattern recognition test, the proposed mIAR(modified integrated mean autoregressive model) technique improves the recognitions rate around 17-21% compared with other the AR and cepstral methods.

Keywords

References

  1. G. Hefftner and G. G. Jaros, 'The electromyogram (EMG) as a control signal for functional neuromuscular stimulation-Pt.Ⅱ: Practical Demonstration of the EMG signature discrimination system,' IEEE Trans. on Biomed. Eng., vol. 35, pp. 238-242, April. 1988 https://doi.org/10.1109/10.1371
  2. B. Hudgins, P. Parker, and R. N. Scott, 'A new strat egy for multifunction myoelectric control,' IEEE Trans. on Biomed. Eng., vol. 40, pp. 82-94, Jan., 1993 https://doi.org/10.1109/10.204774
  3. W.J. Kang, J.R. Shiu, C.K. Cheng, J.S. Lai, H.W. Tsao, and T.s. Kuo, 'The Effect ofElectrode Arrangement on Spectral Distance Measures for Discrimination of EMG Signal,' IEEE Trans. on Biomed. Eng., vol. 44, pp. 1020-1023, October 1997 https://doi.org/10.1109/10.634653
  4. J.M. Salavedra, E. Masgrau, A. Moreno, J. Estarellas, 'Variable Frame Length Of A Higher Order Speech AR Estimation In A Speech Enhancement System,' IEEE Seventh SP Workshop on Statistical Signal and Array Processing , pp 219-222, June 26-29, 1994
  5. Wen-Rong Wu, Po-Chen Chen, Hwai-Tsu Chang, Chun-Hung Kuo, 'Frame-based subband Kalman Filtering for speech enhancement,' 1998 Fourth International Conference on Signal Processing Proceedings, vol.1, pp 682-685 , 12-16 Oct. 1998 https://doi.org/10.1109/ICOSP.1998.770303
  6. W. Gersch, D. R. Sharpe, 'Estimation of Power Spectra with Finite-Order Autoregressive Models,' IEEE Trans. on Automation Control, vol. 5, pp 367-369, 1973 https://doi.org/10.1109/TAC.1973.1100350
  7. Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing, Prentice-Hall, Inc., 1989
  8. C.S. Pattichis and A.G. Elia, 'Autoregressive and cepstral analyses of motor unit action potentials,' J. Electrogmygr. Kinesiol. Medical Eng. & Physics, vol. 21, pp. 405-419, 1999 https://doi.org/10.1016/S1350-4533(99)00072-7
  9. S. J. Orfanidis, Optimum Signal Processing, McGraw Hill, New York, 1985
  10. Peyton Z., Peebles JR, Probability Random Variables and Random Signal Principles, McGraw-Hill, 1993
  11. Lawrence Rabiner, Biing-Hwnag Juang, Fundamentals of Speech Recognition, Prentice-Hall Inc., pp. 163-166, 1993
  12. X. P. Maldague, Advances in Signal Processing for Nondestructive Evaluation of Materials, Kluwer Academic Publishers, 1993
  13. Jingping Xu, Jingzhi Cheng, Yanjun Wu, 'A Cepstral Method for Analysis of Acoustic Transmission Characteristics of Respiratory System,' IEEE Trans. on Biomedical Eng., vol. 45 5, pp 660-664, May 1998 https://doi.org/10.1109/10.668757
  14. Keinosuke Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, Inc. Second Edition, 1990
  15. Sadaoki Furui, Digital Speech Processing, Synthesis, and Recognition, Marcel Dekker Inc., 1992
  16. Agustine H. Gray. JR. and John D. Markel, 'Distance Measure for Speech Processing.' IEEE Trans. on Biomedical Eng., vol. ASSP-24, No. 5, pp. 380-391, October, 1976
  17. McGill, K. C., 'Optimal resolution of superimposed action potentials.' IEEE Trans. on Biomedical Engineering, vol. 49, pp 640-650, July 2002 https://doi.org/10.1109/TBME.2002.1010847
  18. Lowery, M. M. Stoykov, N.S. Kuiken, T.A., 'Independence of myoelectric control signals examined using a surface EMG model.' IEEE Trans. on Biomedical Engineering vol. 50, pp 789-793, June 2003 https://doi.org/10.1109/TBME.2003.812152