Feature Selection of EMG Signals Based on The Separability Matrix and Rough Set Theory

• 영문명: Feature Selection of EMG Signals Based on The Separability Matrix and Rough Set Theory
• 발행기관: 한국과학기술원 인간친화 복지 로봇 시스템 연구센터
• 저자명: Jeong-Su Han Zeungnam Bien
• 간행물 정보: 『International Journal of Assistive Robotics and Mechatronics』International Journal of Human-friendly Welfare Robotic Systems Vol.6 No.3, 24~30쪽, 전체 7쪽
• 주제분류: 공학 > 제어계측공학
• 파일형태: PDF
• 발행일자: 2005.10.01

영문 초록

  This work considers the problem of selecting features of EMG signals in order to effectively use EMG signals to command the operations of mechatronic devices and systems. We first note that EMG signals form patterns that depend much on the subject from which the signals are extracted. We then introduce a notion called “separability matrix” in order to check the effectiveness of a given set of features for classification. Based on the separability matrix and in the form of the rough set theory, we present an algorithm of selecting features to minimize the subject-dependency. It is shown through an experimental study that the induced feature set obtained by the proposed feature selection algorithm has less subject-dependency than the other existing methods.  This work considers the problem of selecting features of EMG signals in order to effectively use EMG signals to command the operations of mechatronic devices and systems. We first note that EMG signals form patterns that depend much on the subject from which the signals are extracted. We then introduce a notion called “separability matrix” in order to check the effectiveness of a given set of features for classification. Based on the separability matrix and in the form of the rough set theory, we present an algorithm of selecting features to minimize the subject-dependency. It is shown through an experimental study that the induced feature set obtained by the proposed feature selection algorithm has less subject-dependency than the other existing methods.

목차

Abstract
Ⅰ. INTRODUCTION
Ⅱ. SUBJECT DEPENDENCY OF EMG SIGNALS IN PATTERN CLASSIFICATION
Ⅲ. PROPOSED FEATURE SELECTION METHOD OF EMG SIGNALS
Ⅳ. EXPERIMENTAL RESULTS
Ⅴ. CONCLUSION REMARKS
ACKNOWLEDGEMENTS
REFERENCE
APPENDIX

키워드

Feature Selection Separability Matrix EMG Signals Pattern Classification FMMNN