title: Pattern recognition of surface EMG biological signals by means of Hilbert spectrum and fuzzy clustering.
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.
authors: Faculty of Engineering, Research Group on Control and Instrumentation, Universidad Tecnologica de Pereira, Pereira, Colombia. rdpinzonm@utp.edu.co
authors: Ruben-Dario
authors: RD
authors: Pinzon-Morales
authors: Katherine-Andrea
authors: KA
authors: Baquero-Duarte
authors: Alvaro-Angel
authors: AA
authors: Orozco-Gutierrez
authors: Victor-Hugo
authors: VH
authors: Grisales-Palacio
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology; D016208:Databases, Factual; D004576:Electromyography / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D006225:Hand / Q000502:physiology; D006801:Humans; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*; D012815:Signal Processing, Computer-Assisted