Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 18, Issue 1, 2010, Pages 49-57

  Hargrove, Levi J ^a,b   Scheme, Erik J ^c   Englehart, Kevin B ^c   Hudgins, Bernard S ^c  

^a REHABILITATION INSTITUTE OF CHICAGO   (United States)

^b NORTHWESTERN UNIVERSITY   (United States)

^c UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

Artificial arms; Electromyogram (EMG); Myoelectric signal (MES); Myoelectric signals; Pattern recognition; Powered prostheses

Indexed keywords

ARTIFICIAL ARMS; BINARY CLASSIFICATION; CLASSIFICATION ERRORS; ELECTROMYOGRAM; EMBEDDED IMPLEMENTATION; LINEAR DISCRIMINANT ANALYSIS; MODE-SWITCHING; MYOELECTRIC CONTROL; MYOELECTRIC SIGNALS; NOVEL PATTERNS; USABILITY TESTS; VIRTUAL ENVIRONMENTS;

ARTIFICIAL LIMBS; CONTROL THEORY; DISCRIMINANT ANALYSIS; FISHER INFORMATION MATRIX; IMAGE RETRIEVAL; PATTERN RECOGNITION SYSTEMS; VIRTUAL REALITY;

MYOELECTRICALLY CONTROLLED PROSTHETICS;

ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED THERAPY; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; ELECTROMYOGRAPHY; FEEDBACK SYSTEM; HUMAN; LIMB PROSTHESIS; METHODOLOGY; MUSCLE CONTRACTION; PHYSIOLOGY; STATISTICAL MODEL;

ARTIFICIAL LIMBS; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; ELECTROMYOGRAPHY; FEEDBACK; HUMANS; LINEAR MODELS; MODELS, BIOLOGICAL; MUSCLE CONTRACTION; PATTERN RECOGNITION, AUTOMATED; THERAPY, COMPUTER-ASSISTED;

EID: 77649305658     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2009.2039590     Document Type: Article

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