title: Novel method to characterize upper-limb movements based on paraconsistent logic and myoelectric signals.
abstract: This paper presents a novel method that investigates the use of Paraconsistent Artificial Neural Network (PANN) and upper-limb electromyography signals for classification of movements, due to their intrinsic ability to deal with imprecise, inconsistent and paracomplete data. The preliminary study presents promising results in terms of processing time and accuracy. The average classification accuracy for the developed paraconsistent logic method was 76,0±9,1% for 17 distinguish movements and a classification average processing time of 14 ms per movement.
authors.forename: Gabriela W
authors.initials: GW
authors.lastname: Favieiro
authors.forename: Karina O A
authors.initials: KO
authors.lastname: Moura
authors.forename: Alexandre
authors.initials: A
authors.lastname: Balbinot
mesh_terms: D000328:Adult; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008128:Logic; D008297:Male; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D035501:Uncertainty; D034941:Upper Extremity / Q000502:physiology*; D055815:Young Adult