title: Classification of EMG signals using artificial neural networks for virtual hand prosthesis control.
abstract: Computer-based training systems have been widely studied in the field of human rehabilitation. In health applications, Virtual Reality presents itself as an appropriate tool to simulate training environments without exposing the patients to risks. In particular, virtual prosthetic devices have been used to reduce the great mental effort needed by patients fitted with myoelectric prosthesis, during the training stage. In this paper, the application of Virtual Reality in a hand prosthesis training system is presented. To achieve this, the possibility of exploring Neural Networks in a real-time classification system is discussed. The classification technique used in this work resulted in a 95% success rate when discriminating 4 different hand movements.
authors.affiliation: Virtual and Augmented Reality Research Group, Faculty of Electrical Engineering from Federal University of Uberlândia, Brazil. mattioli.fernando@gmail.com
authors.forename: Fernando E R
authors.initials: FE
authors.lastname: Mattioli
authors.forename: Edgard A
authors.initials: EA
authors.lastname: Lamounier
authors.forename: Alexandre
authors.initials: A
authors.lastname: Cardoso
authors.forename: Alcimar B
authors.initials: AB
authors.lastname: Soares
authors.forename: Adriano O
authors.initials: AO
authors.lastname: Andrade
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D003199:Computer Systems; D004576:Electromyography / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000294:innervation* / Q000473:pathology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D015398:Signal Transduction; D012867:Skin / Q000473:pathology; D014584:User-Computer Interface