Application of Quaternion Neural Network to EMG-Based Estimation of Forearm Motion
نویسندگان
چکیده
This paper presents the Application of Quaternion Neural Network (QNN) to Electromyography (EMG) based Estimation of Forearm Motion. Motion of human body can be modeled as a set of rotations in three-dimensional space by various joints. The aim of this research is to show the efficiency of QNN in estimation from EMG. We are trying to learn, estimate and simulate combined motion using QNN as well as comparing the estimation efficiency with the feedforward neural network (FNN). We are measuring EMG signal of target muscles while performing basic forearm motion. The efficiency of the estimation then determines by simulation with combine motion of forearm. It is expected in better performance of estimation in QNN compare to FNN.
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