Unlike robotic systems, humans excel in various tasks by taking advantage of their intrinsic compliance, 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. An artificial neural network (ANN) model was developed to relate the EMG and joint motion to joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. Experimental and simulation results confirmed the feasibility of the developed ANN model.
['Byungduk Kang', 'Byungchan Kim', 'Shinsuk Park', 'Hyunkyu Kim']
Modeling of Artificial Neural Network for the Prediction of the Multi-Joint Stiffness in Dynamic Condition