로봇학회논문지 제15권 제1호(통권 제55호) (p.62-69)

원형 구조 알고리즘을 이용한 근전도 패턴 인식 및 분류

Electromyography Pattern Recognition and Classification using Circular Structure Algorithm
키워드 :
Electromyography,Pattern Recognition,Classification,Deep Learning

목차

Abstract
1. 서 론
2. 원형 구조 알고리즘 제안
   2.1 원형 구조 기반 데이터 수집
   2.2 기계학습 알고리즘
3. 실험 및 결과
   3.1 실험 대상 및 학습 환경
   3.2 실험 결과
4. 결 론
References

초록

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.