EMG Control for a Five-fingered Underactuated Prosthetic Hand Based on Wavelet Transform and Sample Entropy

Abstract

A new five-fingered underactuated prosthetic hand control system is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines VLR (variable learning rate) based neural network with wavelet transform and sample entropy. 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 prosthetic hand can achieve more prehensile postures such as power grasp, fingertip 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 high recognition capability