Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지

The Virtual Robot Arm Control Method by EMG Pattern Recognition using the Hybrid Neural Network System

혼합형 신경회로망을 이용한 근전도 패턴 분류에 의한 가상 로봇팔 제어 방식

  • Published : 2006.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a method of virtual robot arm control by EMG pattern recognition using the proposed hybrid system. The proposed hybrid system is composed of the LVQ and the SOFM, and the SOFM is used for the preprocessing of the LVQ. The SOFM converts the high dimensional EMG signals to 2-dimensional data. The EMG measurement system uses three surface electrodes to acquire the EMG signal from operator. Six hand gestures can be classified sufficiently by the proposed hybrid system. Experimental results are presented that show the effectiveness of the virtual robot arm control by the proposed hybrid system based classifier for the recognition of hand gestures from EMG signal patterns.

본 논문은 근전도 패턴 인식에 의한 가상 로봇팔 제어 방식을 제안한다. 고차원의 근전도 신호를 정밀하게 분류하기 위하여 혼합형 신경 회로망 방식을 사용한다. 혼합형 신경회로망은 SOFM과 LVQ로 구성되고, 고차원의 EMG 신호를 2차원 데이터로 변환한다. 3개의 표면 전극을 이용하여 EMG 신호를 측정 한다. 제안한 혼합 시스템을 이용하여 한글 자음 6개의 수화 신호를 분류한다. 가상 로봇팔 실험을 통해서 제안한 혼합 시스템을 이용한 수신호의 EMG 패턴 인식의 유용성을 확인하였다.

Keywords

References

  1. F. H. Y. Chan, Y. S. Yang, F. K. Lam, Y. T. Zhang, P. A. Parker, 'Fuzzy EMG classification for prosthesis control,' IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 3, pp. 305-311, 2000 https://doi.org/10.1109/86.867872
  2. C. I. Christodou1ou, C. S. Pattichis, 'Unsupervised pattern recognition for the classification of EMG signals,' IEEE Transactions on Biomedical Engineering, vol. 46, no. 2, pp. 169-178, 1999 https://doi.org/10.1109/10.740879
  3. J. Duchene, J. Y. Hogrel, 'A model of EMG generation,' IEEE Transactions on Biomedical Engineering, vol. 47, no. 2, pp. 192-201, 2000 https://doi.org/10.1109/10.821754
  4. A. Merlo, D. Farina, R. Merletti, 'A fast and reliable technique for muscle activity detection from surface EMG signals,' IEEE Transactions on Biomedical Engineering. vol. 50, no. 3, pp. 316-323, 2003 https://doi.org/10.1109/TBME.2003.808829
  5. D. Nishikawa, W. Yu, H. Yokoi, Y. Kakazu, 'On-Line Learning Method for EMG Prosthetic Hand Control,' Electronics and Communications in Japan, vol. 84, no. 10, pp. 35-46, 2001 https://doi.org/10.1002/ecja.1051
  6. Haganm Demuth Beale, 'Neural Network Design', PWS Publishing Company, 1995
  7. T. Kohonen, 'The self-organizing map,' Springer, 2000