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  • One size doesnt fit all: Supervised machine learning classification in athlete-monitoring

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    Worsey462436Accepted.pdf (572.8Kb)
    Author(s)
    Worsey, Matthew Thomas Owen
    Espinosa, Hugo G
    Shepherd, Jonathan Bruce
    Thiel, David V
    Griffith University Author(s)
    Espinosa, Hugo G.
    Shepherd, Jonathan
    Worsey, Matthew T.
    Thiel, David V.
    Year published
    2021
    Metadata
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    Abstract
    Athlete movement data is integral for optimizing athlete-performance and can lead to reduced fatigue and in turn can mitigate injury risk. There is a substantial amount of scientific literature which investigates the ability of computer-vision and inertial sensor technologies to classify sport-specific movements. The coupling of automatic sport action labelling and athlete-monitoring data can significantly enhance athlete work-load monitoring. Two recent systematic reviews of the literature, pertinent to sport-specific movement classification, revealed that the majority of journal articles use athlete-dependent classification ...
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    Athlete movement data is integral for optimizing athlete-performance and can lead to reduced fatigue and in turn can mitigate injury risk. There is a substantial amount of scientific literature which investigates the ability of computer-vision and inertial sensor technologies to classify sport-specific movements. The coupling of automatic sport action labelling and athlete-monitoring data can significantly enhance athlete work-load monitoring. Two recent systematic reviews of the literature, pertinent to sport-specific movement classification, revealed that the majority of journal articles use athlete-dependent classification model training and evaluation methods. These methods can significantly enhance model classification performance, particularly with movements which have high inter-athlete technique variation. This is because it enables models to learn features distinctive to all athletes during training. This manuscript details the training and evaluation of supervised machine learning models to automatically classify running surface (athletics track, hard sand and soft sand) using features extracted from an upper-back inertial measurement unit sensor. Possible classification performance enhancement is demonstrated by comparing athlete independent and athlete dependent supervised machine learning methods. Using athlete dependent methods significantly increased the classification performance in terms of weighted average precision, recall, F1-score and accuracy (p < 0.05).
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    Journal Title
    IEEE Sensors Letters
    DOI
    https://doi.org/10.1109/lsens.2021.3060376
    Copyright Statement
    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Human Movement and Sports Sciences
    Biomedical Engineering
    Publication URI
    http://hdl.handle.net/10072/402464
    Collection
    • Journal articles

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