An advanced hybrid machine learning approach for assessment of the change of gait symmetry

WSEAS Transactions on Computers

Volumn 8, Issue 9, 2009, Pages 1522-1532

  Wu, Jianning ^a  

^a FUJIAN NORMAL UNIVERSITY   (China)

Author keywords

Gait analysis; Gait classification; Gait symmetry; Kernel principal component analysis; Kinetic gait data; Support vector machine

Indexed keywords

BASIC IDEA; BINARY CLASSIFICATION; CLASSIFICATION MODELS; CLASSIFICATION PERFORMANCE; CLINICAL DIAGNOSTICS; DATA SUPPORT; EFFECTIVE TOOL; FORCE PLATFORM; FUNCTIONAL CHANGES; GAIT CLASSIFICATION; GAIT FUNCTIONS; GAIT SYMMETRY; GENERALIZATION PERFORMANCE; GROUND FORCES; HUMAN GAIT; HUMAN LOWER EXTREMITY; HYBRID MACHINE LEARNING; HYBRID MODEL; KERNEL PRINCIPAL COMPONENT ANALYSIS; LOWER LIMB; NON-LINEAR DYNAMICS; NORMAL WALKING; POLYNOMIAL KERNELS; PRINCIPAL COMPONENTS; QUANTITATIVE ASSESSMENTS; RIGHT AND LEFT; STRAIN GAUGE; TEST RESULTS; TRAINING SETS;

DATA HANDLING; GAGES; GAIT ANALYSIS; IMAGE RETRIEVAL; ROBOT LEARNING; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 70450235397     PISSN: 11092750     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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