Prosthesis Control Using a Nearest Neighbor Electromyographic Pattern Classifier
David C. Dening, F.G. Gray, Robert M. Haralick · IEEE Transactions on Biomedical Engineering · 1983
An investigation was conducted into the feasibility of applying a nearest neighbor algorithm to the problem of recognizing electromyographic (EMG) signal patterns for prosthesis control. A nearest neighbor algorithm correctly identified arm motions as belonging to one of six pattern classes from 72 to 100 percent of the time. A condensed nearest neighbor classifier was constructed to determine what minimum number of vectors was necessary in the look-up table.