J164025 Remarks on Control of Communication Robot Hand Gripper using Surface Electromyogram

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  • J164025 表面筋電位によるコミュニケーションロボットのハンドグリッパ制御に関する一考察([J164-02]医療・健康・福祉のためのセンシングおよびロボティクス(2))

Abstract

Many studies of human-machine interface systems based on electromyogram(EMG) for controlling robot manipulators and prosthetic hands have conducted worldwide. This paper investigates a gesture classification of an upper limb from surface EMG sensors are mounted on the user's upper limb and four motions of elbow, i.e. flexion, extension, pronation and supination, are considered. In gesture classification experiments, the averaged classification rates are 74%, 66% and 53% can be achieved by using the RBF network, neural network and liner multiple regression, respectively. The experimental result shows the feasibility of the RBF network for this task.

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