Affiliations: [a] Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada | [b] Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
Note: [] Address for correspondence: Kevin Englehart, Ph.D., P. Eng., Associate Professor, Electrical and Computer Engineering, Associate Director, Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada E3B 5A3. E-mail: [email protected].
Abstract: This work represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses pattern recognition to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The continuous classifier is optimized with respect to the feature set and classifier used, and post-processing of the decisions to eliminate spurious errors.
