A multi-class proportional myocontrol algorithm for upper limb prosthesis control: Validation in real-life scenarios on amputees

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 23, Issue 5, 2015, Pages 827-836

  Amsuess, Sebastian ^a   Goebel, Peter ^b   Graimann, Bernhard ^b   Farina, Dario ^a  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

^b Otto Bock HealthCare GmbH   (Germany)

Author keywords

Pattern recognition; Physical prosthesis control; Proportional estimation; Prosthesis control; User centered testing

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ARTIFICIAL LIMBS; BIOLOGICAL ORGANS; CHEMICAL ACTIVATION; DEGREES OF FREEDOM (MECHANICS); DESIGN; ELECTROMYOGRAPHY; LEARNING SYSTEMS; MECHANICS; MUSCLE; OPTIMIZATION; PATTERN RECOGNITION; REAL TIME CONTROL; ROBUSTNESS (CONTROL SYSTEMS);

COMMON SPATIAL PATTERNS; MACHINE LEARNING METHODS; MULTIPLE DEGREES OF FREEDOM; PROSTHESIS CONTROL; STATE-OF-THE-ART METHODS; STATE-OF-THE-ART TECHNIQUES; TECHNOLOGICAL CHALLENGES; USER-CENTERED;

PROSTHETICS;

ALGORITHM; AMPUTATION STUMP; AUTOMATED PATTERN RECOGNITION; CLINICAL TRIAL; DEVICE FAILURE ANALYSIS; DISABLED PERSON; ELECTROMYOGRAPHY; FEEDBACK SYSTEM; HAND; HUMAN; LIMB PROSTHESIS; PATHOPHYSIOLOGY; PROCEDURES; PROSTHESIS DESIGN; REHABILITATION; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VALIDATION STUDY;

ALGORITHMS; AMPUTATION STUMPS; AMPUTEES; ARTIFICIAL LIMBS; ELECTROMYOGRAPHY; EQUIPMENT FAILURE ANALYSIS; FEEDBACK, PHYSIOLOGICAL; HAND; HUMANS; PATTERN RECOGNITION, AUTOMATED; PROSTHESIS DESIGN; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84949972377     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2014.2361478     Document Type: Article

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