A wavelet-based continuous classification scheme for multifunction myoelectric control

This work represents an ongoing investigation of dexterous and natural cont rol 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 descri bed that demonstrates greater accuracy than in previous work. Fundamental t o the success of this method is the use of a wavelet-based feature set, red uced in dimension by principal components analysis. Further, it is shown th at four channels of myoelectric data greatly improve the classification acc uracy, as compared to one or two channels. It is demonstrated that exceptio nally accurate performance is possible using the steady-state myoelectric s ignal. Exploiting these successes, a robust online classifier is constructe d, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natur al and efficient means of myoelectric control than one based on discrete, t ransient bursts of activity.