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EMG Control for a Five-fingered Prosthetic Hand Based on Wavelet Transform and Autoregressive Model

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6 Author(s)
Jingdong Zhao ; Robotics Inst. of Harbin Inst. of Techn., Harbin Inst. of Technol. ; Zongwu Xie ; Li Jiang ; Hegao Cai
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A five-fingered underactuated prosthetic hand controlled by surface electromyographic (EMG) signals is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines Levenberg-Marquardt (LM) or variable learning rate (VLR) based neural network with parametric autoregressive (AR) model and wavelet transform. This motion pattern classifier can successfully identify flexion and extension of the thumb, the index finger and the middle finger, by measuring the surface EMG signals through three electrodes mounted on the flexor digitorum profundus, flexor pollicis longus and extensor digitorum. Furthermore, via continuously controlling single finger's motion, the five-fingered underactuated prosthetic hand can achieve more prehensile postures such as power grasp, centralized grip, fingertip grasp, cylindrical grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its fast learning speed, high recognition capability

Published in:

Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on

Date of Conference:

25-28 June 2006