Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors

Annals of Biomedical Engineering

Volumn 42, Issue 3, 2014, Pages 600-612

  Zhang, Jian ^a   Lockhart, Thurmon E ^a,b   Soangra, Rahul ^b  

^a State University   (United States)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Falls; Fatigue; Gait; Jerk cost; Machine learning; Support vector machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); FATIGUE OF MATERIALS; FREQUENCY DOMAIN ANALYSIS; GAIT ANALYSIS; INERTIAL NAVIGATION SYSTEMS; KINEMATICS; LEARNING SYSTEMS; MUSCLE; SUPPORT VECTOR MACHINES;

FALLS; FEATURE SELECTION METHODS; GAIT; INERTIAL MEASUREMENT UNIT; JERK-COST; SUPERVISED MACHINE LEARNING; SUPPORT VECTOR MACHINE (SVMS); TIME AND FREQUENCY DOMAINS;

PATTERN RECOGNITION;

ACCURACY; ADULT; ARTICLE; BIOSENSOR; BODY POSITION; DATA PROCESSING; DISEASE CLASSIFICATION; DYNAMOMETER; EQUIPMENT; EXERCISE; FEMALE; GAIT; HUMAN; HUMAN EXPERIMENT; INFORMATION; KERNEL METHOD; KINEMATICS; MACHINE LEARNING; MALE; MUSCLE FATIGUE; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; WALKING;

ADOLESCENT; ARTIFICIAL INTELLIGENCE; BIOMECHANICAL PHENOMENA; FEMALE; GAIT; HUMANS; LOWER EXTREMITY; MALE; MODELS, BIOLOGICAL; MUSCLE FATIGUE; MUSCLE, SKELETAL; WALKING;

EID: 84898639202     PISSN: 00906964     EISSN: 15739686     Source Type: Journal    
DOI: 10.1007/s10439-013-0917-0     Document Type: Article

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