High-accuracy recognition of muscle activation patterns using a hierarchical classifier

Systems based on Surface Electromyography (sEMG) signals require some form of machine learning algorithm for recognition and classification of specific patterns of muscle activity. These algorithms vary in terms of the number of signals, feature selection, and the classification algorithm used. In our previous work, a technique for recognizing muscle patterns using a single sEMG signal, called Guided Under-determined Source Signal Separation (GUSSS), was introduced. This technique relied on a very small number of features to achieve good classification accuracies for a small number of gestures. In this paper, an enhanced version called Hierarchical GUSSS (HiGUSSS) was developed to allow for the classification of a large number of hand gestures while preserving a high classification accuracy.

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