Myoelectric Control Strategies for a Human Upper Limb Prosthesis

Abstract

Myolectric control is nowadays the most used approach for electrically-powered upper limb prostheses. The myoelectric controllers use electromyographic (EMG) signals as inputs. These signals, collected from the surface of the skin, have to be preprocessed before being used as inputs for the controller. In this paper we present a classifier for surface electromyographical signals based on an autoregressive (AR) model representation and a neural network, and two myoelectric control strategies based on Finite State Machine. The results have shown that combining a low-order AR model with a feed-forward neural network, a rate of classification ranging from 91% to 98% can be achieved, while keeping the computational cost low. One of the main advantages of the proposed strategy is the reduced effort required to the patient for controlling the prosthetic device.