Automated detection of instantaneous gait events using time frequency analysis and manifold embedding

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 21, Issue 6, 2013, Pages 908-916

  Aung, Min S H ^a   Thies, Sibylle B ^b   Kenney, Laurence P J ^b   Howard, David ^b   Selles, Ruud W ^c   Findlow, Andrew H ^b   Goulermas, John Y ^a  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

^b UNIVERSITY OF SALFORD   (United Kingdom)

^c ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

Accelerometry; classification; gait event detection; signal segmentation

Indexed keywords

ACCELEROMETRY; CONTINUOUS WAVELET TRANSFORM; EVENT DETECTION; EVENT DETECTION ALGORITHM; GAUSSIAN MIXTURE MODEL; SIGNAL SEGMENTATION; TIME FREQUENCY ANALYSIS; TRIAXIAL ACCELEROMETER;

ACCELEROMETERS; BIOMECHANICS; CLASSIFICATION (OF INFORMATION); WALKING AIDS;

SIGNAL DETECTION;

ACCELERATION; ALGORITHM; AMBULATORY MONITORING; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; EQUIPMENT; EQUIPMENT DESIGN; EQUIPMENT FAILURE; GAIT; HUMAN; METHODOLOGY; MICROELECTROMECHANICAL SYSTEM; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ACCELERATION; ALGORITHMS; ARTIFICIAL INTELLIGENCE; EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; GAIT; HUMANS; MICRO-ELECTRICAL-MECHANICAL SYSTEMS; MONITORING, AMBULATORY; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84888109321     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2013.2239313     Document Type: Article

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