Parallel cascade classification of myoelectric signals

Recently, it was shown that the myoelectric signal (MES), recorded during the initial phase of a contraction, has considerable structure which is distinct for contractions producing different limb functions. This has enabled distinctive features to be extracted from the signals which were used to distinguish between contraction types using an artificial neural network. In the present paper, we show that parallel cascade indentification can be used to distinguish between contraction types, with the following benefits. The parallel cascade requires very little data to learn to distinguish contraction type accurately. Training is very rapid, and feature extraction is unnecessary so that sampled raw MES data can be used. Results of using parallel cascade identification to distinguish between medial and lateral humeral rotation are presented