Continuous Time Normalized Signal Trains for a Better Classification of Myoelectric Signals

Abstract

State-of-the-art hand prostheses differ from those of previous generations, in that more hand positions and programmable gestures are available [4]. As an example, consider multi-finger prostheses like the i-limb^TM ultra from Touch Bionics or the Bebionic^TM Hand from RSL Steeper. Both prosthetic effectors are primarily controlled by myoelectric signals, derived with two or more cutaneously applied sensors, placed atop residual muscles. After preprocessing and classification of these signals, three to five different movement states or hand positions can be accurately distinguished. Zardoshti-Kermani et al. [5] show that the classification becomes increasingly difficult as the number of gestures grows, because decision spaces and feature clusters overlap [3]. Since static separation becomes increasingly difficult, Hudgins et al. [3] and Attenberger [1] used time-dependencies inherent to electromyographic (EMG) signals to improve classification.