The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

A Study on the EMG Pattern Recognition Using SOM-TVC Method Robust to System Noise

시스템잡음에 강건한 SOM-TVC 기법을 이용한 근전도 패턴 인식에 관한 연구

  • 김인수 (서울시립대학 전자전기컴퓨터공학부) ;
  • 이진 (삼척대학교 제어계측공학과) ;
  • 김성환 (서울시립대학 전자전기컴퓨터공학부)
  • Published : 2005.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

Keywords

References

  1. C.L. Lin, W.J. Kang, C.T. Hu and S.T. Young, 'Improved EMG pattern recognition using the distribution plot of cepstrum' , in proc. of Int. conf. of IEEE Eng. in Medicine and Biology Society, vol. 20, pp. 2620-2622, 1998 https://doi.org/10.1109/IEMBS.1998.744996
  2. A. Asres, H. Dou, Z. Zhou, Y. Zhang and S. Zhu, 'A combination of AR and neural network technique for EMG pattern identification,' int. conf. of IEEE Eng. in Medicine and Biology Society, pp. 1464-1465. 1996 https://doi.org/10.1109/IEMBS.1996.647506
  3. W.J. Kang, J.R. Shiu, C.K. Cheng, J.S. Lai. H.W. Tsao and T.S. Kuo, 'The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition,' IEEE Trans. on Biomed. Eng., vol. 42, no. 8, Aug., 1995 https://doi.org/10.1109/10.398638
  4. K. Englehart, B. Hudgins, and Philip A. Parker, 'A wavelet-based continuous classification scheme for multifunction myoelectric control,' IEEE Trans. Biomed. Eng., vol. 48, no. 3, pp. 302-311, March 2001 https://doi.org/10.1109/10.914793
  5. H.P. Huang and C.Y. Chen, 'Development of a myoelectric discrimination system for a multi-degree prosthetic hand,' IEEE International Conf. on Robotics & Automation, vol. 3, pp. 2392-2397, May, 1999 https://doi.org/10.1109/ROBOT.1999.770463
  6. K. Kuribayashi, S. Shimizu, A. Kawachi and T. Taniguchi, 'A discrimination system using neural network for SMA prosthesis,' in proc. of int. conf. of IEEE on Intelligent Robots and Systems, vol. 3, 1994 https://doi.org/10.1109/IROS.1994.407610
  7. K. I. Diamantaras, S. Y. Kung, 'Principal Component Neural Networks: Theory and Application', Wiley, 1996
  8. J. M. Killer, M. R. Gray, and J. A. Givens, JR, ' A fuzzy k-nearest neighbor algorithm', IEEE Transactions On Systems, Man, and Cybernetics, Vol. 15, no. 4, 1986
  9. Han-Pang Huang, Yi-hung Lju, Li-Wei Lju and Chun-Shin Wong 'EMG Classificaton for Prehensile Postures Using Cascaed Architecture of Neural Networks with Self-Organizing maps', IEEE International Conf. on Robotics & Automation, 2003
  10. Ainishet Asres, Huifang Dou, 'A Combination of AR and neural network Technique for EMG Pattern Identification',EEE International Conf. Engineering in Medicine and biology Society, 1996 https://doi.org/10.1109/IEMBS.1996.647506
  11. K. Ito, T. Tsuji, A. Kato and M. Ito, 'EMG pattern classification for a prosthetic forearm with three degrees of freedom,' in proc. of int. workshop of IEEE on robot and human communication, pp. 69-74, 1992 https://doi.org/10.1109/ROMAN.1992.253907
  12. T. Kohonen, 'The self-organizing map', Proc. of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990 https://doi.org/10.1109/5.58325
  13. 전창익, '인공의수 제어를 위한 근전도 신호의 진폭 추정과 패턴 인식 기법에 관한 연구', 서울 시립대학교 박사 논문
  14. 임중규, 'EMG 신호의 패턴 분류를 위한 간단한 SOM 방식', 전자공학회논문지, 제 38권 4호, 2001
  15. Simon Haykin, 'Neural Networks A comprehensive foundation', Prentice-Hall, 1999