Pattern Recognition of Surface EMG Biological Signals by Means of Hilbert Spectrum and Fuzzy Clustering View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011-03-15

AUTHORS

Ruben-Dario Pinzon-Morales , Katherine-Andrea Baquero-Duarte , Alvaro-Angel Orozco-Gutierrez , Victor-Hugo Grisales-Palacio

ABSTRACT

A novel method for hand movement pattern recognition from electromyography (EMG) biological signals is proposed. These signals are recorded by a three-channel data acquisition system using surface electrodes placed over the forearm, and then processed to recognize five hand movements: opening, closing, supination, flexion, and extension. Such method combines the Hilbert-Huang analysis with a fuzzy clustering classifier. A set of metrics, calculated from the time contour of the Hilbert Spectrum, is used to compute a discriminating three-dimensional feature space. The classification task in this feature-space is accomplished by a two-stage procedure where training cases are initially clustered with a fuzzy algorithm, and test cases are then classified applying a nearest-prototype rule. Empirical analysis of the proposed method reveals an average accuracy rate of 96% in the recognition of surface EMG signals. More... »

PAGES

201-209

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4419-7046-6_20

DOI

http://dx.doi.org/10.1007/978-1-4419-7046-6_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007294754

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21431560


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