User training for pattern recognition-based myoelectric prostheses: Improving phantom limb movement consistency and distinguishability

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 22, Issue 3, 2014, Pages 522-532

  Powell, Michael A ^a   Kaliki, Rahul R ^b   Thakor, Nitish V ^a  

^a Johns Hopkins University   (United States)

^b Infinite Biomedical Technologies   (United States)

Author keywords

Electromyography (EMG); motor learning; pattern recognition; prosthetics; rehabilitation; therapy

Indexed keywords

BIOFEEDBACK; ELECTROMYOGRAPHY; PATIENT REHABILITATION; PATTERN RECOGNITION; SOURCE SEPARATION; VIRTUAL REALITY;

DISTINGUISHABILITY; HIGH DIMENSIONAL DATA; LONGITUDINAL STUDY; MOTOR LEARNING; MYOELECTRIC PROSTHESIS; PROSTHESIS CONTROL; THERAPY; TRAINING SESSIONS;

PROSTHETICS;

ADULT; ALGORITHM; AMPUTATION; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; ELECTROMYOGRAPHY; FEMALE; FOREARM; HUMAN; MALE; MIDDLE AGED; MOVEMENT (PHYSIOLOGY); PHANTOM LIMB; PHYSIOLOGY; PROCEDURES; PROSTHESIS;

ADULT; ALGORITHMS; AMPUTATION; ARTIFICIAL INTELLIGENCE; ELECTROMYOGRAPHY; FEMALE; FOREARM; HUMANS; MALE; MIDDLE AGED; MOVEMENT; PATTERN RECOGNITION, AUTOMATED; PHANTOM LIMB; PROSTHESIS DESIGN;

EID: 84900395735     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2013.2279737     Document Type: Article

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