Support vector machines for detecting age-related changes in running kinematics

Journal of Biomechanics

Volumn 44, Issue 3, 2011, Pages 540-542

  Fukuchi, Reginaldo K ^a   Eskofier, Bjoern M ^a   Duarte, Marcos ^b   Ferber, Reed ^a  

^a UNIVERSITY OF CALGARY   (Canada)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Ageing; Kinematics; Running; SVM

Indexed keywords

ACCURACY RATE; AGE GROUPS; AGE-RELATED; AGE-RELATED CHANGES; AGEING; BIOMECHANICAL GAIT; CLASSIFICATION ALGORITHM; CLASSIFICATION APPROACH; CLASSIFICATION PERFORMANCE; COMBINED INFORMATIONS; DATA MINING TECHNIQUES; FEATURE SELECTION ALGORITHM; GENERAL APPROACH; INFERENTIAL STATISTICS; KERNEL METHODS; KINEMATIC VARIABLES; LINEAR KERNEL; LOWER EXTREMITY; RUNNING; RUNNING KINEMATICS; SVM;

ALGORITHMS; BIOMECHANICS; DATA MINING; FEATURE EXTRACTION; GEARS; KINEMATICS;

SUPPORT VECTOR MACHINES;

ACCURACY; ADULT; AGED; AGING; ANKLE PEAK DORSIFLEXION ANGLE; ARTICLE; BIOMECHANICS; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; HUMAN; HUMAN EXPERIMENT; KINEMATICS; KNEE ABDUCTION ANGLE; KNEE FLEXION EXCURSION ANGLE; MALE; MUSCULOSKELETAL SYSTEM PARAMETERS; PEAK KNEE ABDUCTION ANGLE; PRIORITY JOURNAL; RUNNING; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; TIBIAL ROTATION EXCURSION; TOE OUT ANGLE;

ADULT; AGED; AGING; ARTIFICIAL INTELLIGENCE; BIOMECHANICS; GAIT; HUMANS; MALE; RUNNING;

EID: 78651413102     PISSN: 00219290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbiomech.2010.09.031     Document Type: Article

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