Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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EMG Pattern Classification using Soft Computing Techniques and Its Application to the Control of a Rehabilitation Robotic Arm

소프트 컴퓨팅 기법을 이용한 근전도 신호의 패턴 분류와 재활 로봇 팔 제어에의 응용

  • Han, Jeong-Su (Dept. of Electronic Computer Science, Korea Advanced Institute of Science and Technology) ;
  • Kim, Jong-Seong ;
  • Song, Won-Gyeong (Dept. of Electronic Computer Science, Korea Advanced Institute of Science and Technology) ;
  • Bang, Won-Cheol (Dept. of Electronic Computer Science, Korea Advanced Institute of Science and Technology) ;
  • Lee, Hui-Yeong (Chonnam National Universityisy) ;
  • Byeon, Jeung-Nam (Dept. of Electronic Computer Science, Korea Advanced Institute of Science and Technology)
  • 한정수 (한국과학기술원 전자전산학과) ;
  • 김종성 (ETRI 인터넷 정보가전 연구부) ;
  • 송원경 (한국과학기술원 전자전산학과) ;
  • 방원철 (한국과학기술원 전자전산학과) ;
  • 이희영 (전남대학교 전자공학과) ;
  • 변증남 (한국과학기술원 전자전산학과)
  • Published : 2000.11.01
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Abstract

In this paper, a new EMG pattern classification method based on soft computing techniques is proposed to help the disabled and the elderly handle rehabilitation robotic arm systems. First, it is shown that EMG is more useful than existing input devices such as voice, a laser pointer and a keypad in view of naturality, extensibility, and applicability. Then, a new procedure is proposed to select the minimal feature set. As methods of classifying the pre-defined motions, a fuzzy pattern classification and fuzzy min-max neural networks (FMMNN) are designed using the selected features. As results, the motions are recognized with success rates of 83 percent and 90 Percent using fuzzy pattern classification and FMMNN, respectively.

본 논문에서는 소프트 컴퓨팅 기법을 이용한 새로운 근전도 신호 패턴 분류 방법을 제안한다. 재활 로봇시스템에서 기존에 사용되었던 여러 가지 입력 장치(음성, 레이저 포인터, 키패드, 3차원 입력기 등)에 비해 근전도 신호를 이용한 방식이 가지는 장점을 서술한다. 기존의 근전도 신호 분류 방법의 문제점인 사용자 의존성을 줄이기 위해 제안한 사용자 독립적인 특징 선택 방법에 대해 상술한다. 선택된 특징 집합을 이용하여 퍼지 패턴 분류기 및 퍼지 최대-최소 신경망을 구성하여 학습 전(퍼지 패턴 분류기)과 학습 후(퍼지 최대-최소 신경망)에 각각 83%와 90%의 분류 성공률을 얻어 제안된 방법의 유용성을 확인할 수 있었다.

Keywords

References

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