title: Upper-limb movement classification based on sEMG signal validation with continuous channel selection.
abstract: This paper aims to provide an efficient, automatic and auto-adaptive approach to establish a continuous electromyography (EMG) signal monitoring, to constantly identify an optimal electrode assortment to use as input of a pattern recognition method through time. The average classification accuracy for the adaptive input selection method was 83,96±5,79% against 72,06±7,15% in a non-adaptive system. Both systems make use of a neural network to classify 9 distinguish upper-limb movements.
authors.forename: V H
authors.initials: VH
authors.lastname: Cene
authors.forename: G
authors.initials: G
authors.lastname: Favieiro
authors.forename: A
authors.initials: A
authors.lastname: Balbinot
mesh_terms: D000465:Algorithms; D004576:Electromyography; D006801:Humans; D009068:Movement; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D034941:Upper Extremity