Name
Abstract | ||
|---|---|---|
In this paper, we propose a direct torque control method for the prosthetic hand. In order to estimate the joint torque from EMG signals, art artificial neural network by the feedback error learning schema is used. 2-DOF motions, i.e. hand grasping/opening and arm flexion/extension, are picked up. In the experiments, two measurement conditions of EMG signal are prepared: the forearm from which the EMG signal is measured is free or fixed. Then it is verified that the neural network can learn the relation between the EMG signal and the joint torque under the these two measurement conditions. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/IROS.2000.894636 | IROS |
Keywords | Field | DocType |
electromyography,feedback,neural nets,prosthetics,torque control,2-DOF motions,EMG signals,arm flexion,artificial neural network,direct torque control method,feedback error learning,forearm,hand grasping,joint torque,measurement conditions,prosthetic hand control,torque estimation | Torque,Computer science,Control theory,Direct torque control,Electromyography,Control engineering,Forearm,Arm flexion,Artificial neural network | Conference |
Volume | Citations | PageRank |
1 | 3 | 0.61 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satoshi Morita | 1 | 23 | 3.97 |
| Katsunari Shibata | 2 | 56 | 14.65 |
| Xin-Zhi Zheng | 3 | 54 | 10.03 |
| Koji Ito | 4 | 24 | 7.23 |