Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 22, Issue 4, 2014, Pages 735-744

  Gijsberts, Arjan ^a   Atzori, Manfredo ^b   Castellini, Claudio ^c   Müller, Henning ^b   Caputo, Barbara ^a  

^a IDIAP RESEARCH INSTITUTE   (Switzerland)

^b UNIVERSITY OF APPLIED SCIENCES WESTERN SWITZERLAND   (Switzerland)

^c GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Electromyography; machine learning; prosthetics

Indexed keywords

ACCELEROMETERS; ARTIFICIAL INTELLIGENCE; ELECTROMYOGRAPHY; FORECASTING; ITERATIVE METHODS; LEARNING ALGORITHMS; PROSTHETICS; RADIAL BASIS FUNCTION NETWORKS;

CLASSIFICATION ACCURACY; MACHINE LEARNING METHODS; MULTIMODAL CLASSIFIERS; MYOELECTRIC PROSTHESIS; NONLINEAR KERNEL FUNCTIONS; PERFORMANCE CHARACTERISTICS; RADIAL BASIS FUNCTION KERNELS; SURFACE ELECTROMYOGRAPHY;

LEARNING SYSTEMS;

ACTION POTENTIAL; ADULT; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; ELECTROMYOGRAPHY; FEMALE; HAND; HUMAN; JOINT CHARACTERISTICS AND FUNCTIONS; MALE; MOVEMENT (PHYSIOLOGY); MUSCLE CONTRACTION; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ACTION POTENTIALS; ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTROMYOGRAPHY; FEMALE; HAND; HUMANS; MALE; MOVEMENT; MUSCLE CONTRACTION; PATTERN RECOGNITION, AUTOMATED; RANGE OF MOTION, ARTICULAR; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84904334558     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2014.2303394     Document Type: Article

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