로봇학회논문지 제14권 제3호(통권 제53호) (p.211-220)

무학습 근전도 패턴 인식 알고리즘: 부분 수부 절단 환자 사례 연구

Training-Free sEMG Pattern Recognition Algorithm: A Case Study of A Patient with Partial-Hand Amputation
키워드 :
Prosthesis,Pattern Recognition,Surface Electromyogram (sEMG),Unsupervised Learning,Bayesian Probability

목차

Abstract
1. 서 론
2. 방 법
   2.1 무학습 근전도 패턴 인식 알고리즘
   2.2 혼동 행렬을 이용한 분류 가능 동작 분석
   2.3 가우시안 혼합 모델을 이용한 패턴 분석
   2.4 동작 인식률 비교를 위한 교시 학습 알고리즘
3. 실 험
   3.1 실험 구성 및 절차
   3.2 실험 결과 및 논의
4. 결 론
References

초록

Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.