인공신경망과 근전도를 이용한 인간의 관절 강성 예측

Predicting the Human Multi-Joint Stiffness by Utilizing EMG and ANN

  • 강병덕 (고려대학교 기계공학과) ;
  • 김병찬 (고려대학교 기계공학과) ;
  • 박신석 (고려대학교 기계공학과) ;
  • 김현규 (삼성전자주식회사 기술총괄 생산기술연구소)
  • 발행 : 2008.02.29

초록

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human''s superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG) signals and limb position measurements. The EMG signal is the summation of MUAPs (motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN) model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

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