Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Advanced Processing of sEMG Signals for User Independent Gesture Recognition
verfasst von : A. Doswald, F. Carrino, F. Ringeval
Erschienen in: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
While the classification of gestures recorded with sEMG can reach very high recognition rates when the user has trained on the system, performance obtained on unknown users remains low. In this work we attempt to use advanced signal processing and pattern classification methods for improving classification performance of gestures on unknown users. Our approach is to take an existing feature set, add promising features, and use feature selection to prune poor features. For classification we use a support vector machine with a Pearson VII kernel, for which a particle swarm optimization was used to search through its parameter space. Results are presented on the NinaPro database, and show excellent results when the user is known to the system as well as a significant improvement on existing work when the user is unknown.