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논문 기본 정보

자료유형
학술저널
저자정보
장호명 (금오공과대학교) 손정우 (금오공과대학교)
저널정보
Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.37 No.5
발행연도
2020.5
수록면
339 - 345 (7page)
DOI
10.7736/JKSPE.019.138

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초록· 키워드

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In this paper, a prosthetic robot hand was designed and fabricated and experimental evaluation of the realization of basic gripping motions was performed. As a first step, a robot finger was designed with same structural configuration of the human hand and the movement of the finger was evaluated via kinematic analysis. Electromyogram (EMG) signals for hand motions were measured using commercial wearable EMG sensors and classification of hand motions was achieved by applying the artificial neural network (ANN) algorithm. After training and testing for three kinds of gripping motions via ANN, it was observed that high classification accuracy can be obtained. A prototype of the proposed robot hand is manufactured through 3D printing and servomotors are included for position control of fingers. It was demonstrated that effective realization of gripping motions of the proposed prosthetic robot hand can be achieved by using EMG measurement and machine learning-based classification under a real-time environment.

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1. 서론
2. 로봇 의수 설계
3. 손동작 인식
4. 손동작 구현
5. 결론
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