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. © 2011 Springer Science+Business Media, LLC.
CITATION STYLE
Pinzon-Morales, R. D., Baquero-Duarte, K. A., Orozco-Gutierrez, A. A., & Grisales-Palacio, V. H. (2011). Pattern recognition of surface EMG biological signals by means of hilbert spectrum and fuzzy clustering. In Advances in Experimental Medicine and Biology (Vol. 696, pp. 201–209). https://doi.org/10.1007/978-1-4419-7046-6_20
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