A machine learning approach to assessing gait patterns for Complex Regional Pain Syndrome

Medical Engineering and Physics

Volumn 34, Issue 6, 2012, Pages 740-746

  Yang, Mingjing ^a   Zheng, Huiru ^a   Wang, Haiying ^a   McClean, Sally ^a   Hall, Jane ^b   Harris, Nigel ^b  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b University of Bath   (United Kingdom)

Author keywords

Accelerometer; Classification; Complex Regional Pain Syndrome; Feature extraction; Gait analysis

Indexed keywords

CLASSIFICATION ACCURACY; COMPLEX REGIONAL PAIN SYNDROMES; CONTROL SUBJECT; CRITICAL COMPONENT; FUNCTIONAL PERFORMANCE; GAIT PATTERN; LEARNING APPROACH; MACHINE LEARNING METHODS; MULTI LAYER PERCEPTRON; OBJECTIVE ASSESSMENT; PERFORMANCE EVALUATION; PHYSICAL PERFORMANCE; PREDICTION ACCURACY; TEST SETS;

ACCELEROMETERS; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; GAIT ANALYSIS; LEARNING SYSTEMS; PATIENT TREATMENT;

HEALTH;

ACCELEROMETER; ACCURACY; ADULT; AGED; ARTICLE; CLINICAL ARTICLE; COMPLEX REGIONAL PAIN SYNDROME; CONTROLLED STUDY; DISEASE CLASSIFICATION; FEASIBILITY STUDY; FEMALE; GAIT; HUMAN; INFORMATION PROCESSING; MACHINE LEARNING; MALE; MULTILAYER PERCEPTRON NEURAL NETWORK; PHYSICAL PERFORMANCE; PREDICTION; PRIORITY JOURNAL; WALKING DIFFICULTY;

ACCELERATION; ADULT; AGED; ARTIFICIAL INTELLIGENCE; COMPLEX REGIONAL PAIN SYNDROMES; FEASIBILITY STUDIES; FEMALE; GAIT; HUMANS; MALE; MIDDLE AGED; REPRODUCIBILITY OF RESULTS; TIME FACTORS;

EID: 84863310500     PISSN: 13504533     EISSN: 18734030     Source Type: Journal    
DOI: 10.1016/j.medengphy.2011.09.018     Document Type: Article

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