title: Classification of dynamic multi-channel Electromyography by Neural Network.
abstract: Muscles are responsible for movement of the limbs. Muscle contraction is accompanied by electrical activity that is measurable and is the Electromyography (EMG) recording. Due to the complex nature of the signal, detailed analysis and classification is often difficult, especially if the EMG relates to movement. This paper reports the research to determine features of the multi-channel EMG signal recording that correlate with the movement of the hand of the subject. Different processing techniques are reported. It demonstrates integral of the RMS of the signal correlates best with the movement.
authors.affiliation: Biomedical Engineering Lab. School of Electrical and Computer Systems Engineering, RMIT University, Melbourne, Australia. dinesh@rmit.edu.au
authors.forename: D K
authors.initials: DK
authors.lastname: Kumar
authors.forename: N
authors.initials: N
authors.lastname: Ma
authors.forename: P
authors.initials: P
authors.lastname: Burton
mesh_terms: D004576:Electromyography / Q000145:classification*; D006225:Hand / Q000503:physiopathology; D006801:Humans; D009069:Movement Disorders / Q000503:physiopathology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000503:physiopathology*; D009415:Nerve Net / Q000503:physiopathology*; D014953:Wrist / Q000503:physiopathology