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.
journal_name
Adv Exp Med Bioljournal_title
Advances in experimental medicine and biologyauthors
Pinzon-Morales RD,Baquero-Duarte KA,Orozco-Gutierrez AA,Grisales-Palacio VHdoi
10.1007/978-1-4419-7046-6_20subject
Has Abstractpub_date
2011-01-01 00:00:00pages
201-9eissn
0065-2598issn
2214-8019journal_volume
696pub_type
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