A wavelet-based continuous classification scheme for multifunction myoelectric control

TLDR

This work investigates dexterous, natural control of powered upper limbs using myoelectric signals, emphasizing that classification accuracy is critical. The study introduces a novel approach that achieves higher classification accuracy than previous methods. The method employs a wavelet‑based feature set reduced by principal component analysis and a robust online classifier that produces continuous decisions from steady‑state myoelectric data. Four‑channel steady‑state myoelectric data markedly improve accuracy, and the preliminary online scheme indicates a more natural and efficient control than burst‑based approaches.

Abstract

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.