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      • Health, Sport and Human Performance
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      The 'DEEP' Landing Error Scoring System

      Hébert-Losier, Kim; Hanzlíková, Ivana; Zheng, Chen; Streeter, Lee; Mayo, Michael
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      Hebert-Losier (2020) The ‘DEEP’ Landing Error Scoring System.pdf
      Published version, 2.910Mb
      DOI
       10.3390/app10030892
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      Permanent link to Research Commons version
      https://hdl.handle.net/10289/14645
      Abstract
      The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that LESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy of LESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of 10 × 10-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (r = 0.63) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS ≥ 5 errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.
      Date
      2020
      Type
      Journal Article
      Publisher
      MDPI
      Rights
      © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
      Collections
      • Computing and Mathematical Sciences Papers [1454]
      • Health, Sport and Human Performance Papers [125]
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