Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control

IEEE Transactions on Biomedical Engineering

Volumn 61, Issue 4, 2014, Pages 1167-1176

  Amsuss, Sebastian ^a   Goebel, Peter M ^b   Jiang, Ning ^a   Graimann, Bernhard ^b   Paredes, Liliana ^c   Farina, Dario ^a  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

^b Otto Bock HealthCare GmbH   (Germany)

^c Fondazione Ospedale San Camillo IRCCS   (Italy)

Author keywords

Artificial neural networks (ANNs); myoelectric control; pattern recognition (PR); robustness; upper limb prostheses

Indexed keywords

ARTIFICIAL LIMBS; NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS);

CLASSIFICATION ACCURACY; CLASSIFICATION DECISION; EXPERIMENTAL PROTOCOLS; MYOELECTRIC CONTROL; PATTERN RECOGNITION METHOD; POSTPROCESSING ALGORITHMS; SURFACE ELECTROMYOGRAPHY; UPPER LIMB PROSTHESIS;

MUSCLE;

ACCURACY; ADULT; ALGORITHM; ARM PROSTHESIS; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFIER; CLINICAL ARTICLE; CONTROLLED STUDY; DISABLED PERSON; ELECTRODE; ELECTROMYOGRAPHY; FOREARM; HAND; HAND MOVEMENT; HUMAN; IMPEDANCE; MALE; MUSCLE CONTRACTION; PATTERN RECOGNITION; PERCEPTRON; RESPONSE TIME;

ADULT; ALGORITHMS; ARM; ARTIFICIAL LIMBS; ELECTROMYOGRAPHY; FEMALE; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; YOUNG ADULT;

EID: 84897413431     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2013.2296274     Document Type: Article

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