Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control

IEEE Transactions on Biomedical Engineering

Volumn 56, Issue 5, 2009, Pages 1407-1414

  Hargrove, Levi J ^a,b,c   Li, Guanglin ^b,c,d   Englehart, Kevin B ^a   Hudgins, Bernard S ^a  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

^b REHABILITATION INSTITUTE OF CHICAGO   (United States)

^c NORTHWESTERN UNIVERSITY   (United States)

^d SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

Amputee; Electromyography (EMG); Myoelectric; Myoelectric signal (MES); Pattern recognition; Principal components analysis; Prostheses; Tranrsradial

Indexed keywords

AMPUTEE; MYOELECTRIC; MYOELECTRIC SIGNAL (MES); PRINCIPAL COMPONENTS ANALYSIS; PROSTHESES; TRANRSRADIAL;

ARTIFICIAL LIMBS; ELECTROMYOGRAPHY; FEATURE EXTRACTION; MUSCLE; PRINCIPAL COMPONENT ANALYSIS;

MYOELECTRICALLY CONTROLLED PROSTHETICS;

ACCURACY; ARTICLE; ELECTROMYOGRAPHY; LIMB PROSTHESIS; MOTION ANALYSIS SYSTEM; MUSCLE FUNCTION; MYOELECTRIC CONTROL; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS;

ALGORITHMS; AMPUTEES; ELECTROMYOGRAPHY; FOREARM; HUMANS; MUSCLE CONTRACTION; MUSCLE, SKELETAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 67649202809     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2008.2008171     Document Type: Article

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