Levenberg-Marquardt Based Neural Network Control for a Five-fingered Prosthetic Hand

This paper presents a surface Electromyography (EMG) motion pattern classifier which combines Levenberg-Marquardt (LM) based neural network with parametric Autoregressive (AR) model. This motion pattern classifier can successfully identify three types of motion of thumb, index finger and middle finger, by measuring the surface EMG through two electrodes mounted on the flexor digitorum profundus and flexor pollicis longus. 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 and strong robustness.