SIGNAL (programming language)

Computer hardware

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Control system

Computer science

Digital signal processing

Signal processing

Pattern recognition (psychology)

Artificial intelligence

Medicine

Programming language

Physical medicine and rehabilitation

Artificial neural network

Electromyography

Speech recognition

Electrical engineering

IEEE Transactions on Biomedical Engineering

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.

A new strategy for multifunction myoelectric control