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Estimation of forearm movement from EMG signal and application to prosthetic hand control

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3 Author(s)
Morita, S. ; Dept. of Comput. Intelligence & Syst. Sci., Tokyo Inst. of Technol., Yokohama, Japan ; Kondo, T. ; Ito, K.

In this paper, a new approach is examined for controlling the prosthetic hand: the torque control of each joint. The joint torque is estimated from EMG signals using an artificial neural network. The learning system is based on a feedback error learning schema. Two controlled objects were used in the experiments, i.e. a hand dynamic model and prosthetic hand. These experimental results show the possibility to apply the proposed method to the prosthetic hand control under the conditions that the controlled object is a second order delay system.

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Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on  (Volume:4 )

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