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.

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The following is a brief introduction to powered prosthetics and myoelectric control. This paper reviews the present availability and clinical impact of myoelectric prostheses. A significant observation is that these systems have reached a sufficient degree of maturity that they are accepted by many health-care funding agencies as reasonable and cost-effective components of the rehabilitation process. The limitations of both the mechanical systems and the myoelectric controls are discussed in some detail, from the viewpoint of the potential user. Finally, an overview is given of current research in this field with comments on probable directions of development.

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