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  • Maximum-Entropy-Rate Selection of Features for Classifying Changes in Knee and Ankle Dynamics During Running

    Author(s)
    Einicke, Garry Allan
    Sabti, Haider A
    Thiel, David
    Fernandez, Marta
    Griffith University Author(s)
    Thiel, David V.
    Year published
    2018
    Metadata
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    Abstract
    This paper investigates deteriorations in knee and ankle dynamics during running. Changes in lower limb accelerations are analyzed by a wearable musculo-skeletal monitoring system. The system employs a machine learning technique to classify joint stiffness. A maximum-entropyrate method is developed to select the most relevant features. Experimental results demonstrate that distance travelled and energy expended can be estimated from observed changes in knee and ankle motions during 5 km runs.This paper investigates deteriorations in knee and ankle dynamics during running. Changes in lower limb accelerations are analyzed by a wearable musculo-skeletal monitoring system. The system employs a machine learning technique to classify joint stiffness. A maximum-entropyrate method is developed to select the most relevant features. Experimental results demonstrate that distance travelled and energy expended can be estimated from observed changes in knee and ankle motions during 5 km runs.
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    Journal Title
    IEEE Journal of Biomedical and Health Informatics
    DOI
    https://doi.org/10.1109/JBHI.2017.2711487
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Biomechanical engineering
    Publication URI
    http://hdl.handle.net/10072/377520
    Collection
    • Journal articles

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