A new strategy for multifunction myoelectric control.
This paper describes a novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.
Hudgins
B
B
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada.
Parker
P
P
Scott
R N
RN
D000671:Amputation, Surgical / Q000534:rehabilitation; D016477:Artifacts; D015982:Bias; D004594:Electrophysiology; D005069:Evaluation Studies as Topic; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology; D007551:Isotonic Contraction / Q000502:physiology; D008959:Models, Neurological; D009119:Muscle Contraction / Q000502:physiology*; D016571:Neural Networks, Computer; D019736:Prostheses and Implants / Q000592:standards*; D011474:Prosthesis Design / Q000592:standards; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation