Control strategies for functional upper limb prostheses

Abstract

Electrically powered hand protheses are typically controlled with electromyographic (EMG) signals, acquired from muscles of the residual limb. In this chapter we will give an overview on classical EMG control as well as recent developments based on machine learning. Classical approaches utilize two EMG electrodes and allow to control only a single prosthetic function at a time. Machine learning-based approaches utilize more electrodes and can be divided into classification and regression. Classification-based approaches have become recently commercially available and allow a direct access to many prosthetic functions, while classification-based approaches allow for an independent simultaneous control of two degrees of freedom (DOF). Targeted muscle reinnervation is a surgical procedure to acquire additional control sites in the amputees and enables to directly control up to three DOF simultaneously.