A Five-fingered Underactuated Prosthetic Hand Control Scheme

This paper presents a five-fingered underactuated prosthetic hand controlled by surface electromyographic (EMG) signals. The prosthetic hand control part is based on an EMG motion pattern classifier which combines 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