A neural network based classifier for the identification of simple finger motion

The question of whether electromyographic (EMG) data from a single region of the forearm can be used to distinguish between various simple classes of finger motion is examined. Extensive clustering of data is performed to identify useful features for pattern classification. Sets of neural networks are trained to classify movements from each possible pairing of fingers. A multilayered network is constructed to distinguish between all five possible feature types.