Cited by 69Knee injuries and reconstruction techniquesLower Extremity Biomechanics and PathologiesSports Dynamics and Biomechanics

On-field player workload exposure and knee injury risk monitoring via deep learning

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Johnson, William R., Mian, Ajmal, Lloyd, David G., Alderson, Jacqueline A.·2019·Journal of Biomechanics

Abstract

In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plates and associated downstream biomechanical modeling, and many researchers have approached the problem of portability by extrapolating models built on linear statistics. An alternative approach would be to capitalize on recent advances in deep learning. In this study, using the pre-trained CaffeNet convolutional neural network (CNN) model, multivariate regression of marker-based motion capture to 3D KJM for three sports-related movement types were compared. The strongest overall mean correlation to source modeling of 0.8895 was achieved over the initial 33% of stance phase for sidestepping. The accuracy of these mean predictions of the three critical KJM associated with anterior cruciate ligament (ACL) injury demonstrate the feasibility of on-field knee injury assessment using deep learning in lieu of laboratory embedded force plates. This multidisciplinary research approach significantly advances machine representation of real-world physical models with practical application for both community and professional level athletes.

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  1. [6]6. G. T. Burns, J. D. Zendler, and R. F. Zernicke. Wireless insoles to measure ground reaction forces: Step-by-step validity in hopping, walking, and running. ISBS Proceedings Archive , 35(1):295-298, 2017.
  2. [53]53. X. Wei and J. Chai. Videomocap: Modeling physically realistic human motion from monocular video sequences. In ACM Transactions on Graphics (TOG) , volume 29, page 42. ACM, 2010.

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Authors
Johnson, W. R., Mian, A., Lloyd, D. G., & Alderson, J. A.
Affiliations
  • The University of Western Australia (AU)
  • Griffith University (AU)
Corresponding author
Johnson, William R.
Venue
Journal of Biomechanics
Publisher
Elsevier BV
Published
2019-08
Volume
93
Pages
185-193
ISSN
0021-9290 (ISSN-L)
OpenAlex
W2890444304
Cited by
69
References
68
Citations by year
2026: 12 · 2025: 13 · 2024: 11 · 2023: 10 · 2022: 7 · 2021: 9 · 2020: 6 · 2018: 1
FWCI
5.25
Type
journal article
Language
en
Open access
Locations
  • Journal of Biomechanics (publishedVersion)
  • UWA Profiles and Research Repository (UWA) (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Griffith Research Online (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
Australian Research Council
Topics
  • Health Sciences › Medicine › Surgery › Knee injuries and reconstruction techniques
  • Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
  • Physical Sciences › Engineering › Biomedical Engineering › Sports Dynamics and Biomechanics
Keywords
knee joint moments, knee injury risk, anterior cruciate ligament injury, convolutional neural networks, CaffeNet, marker-based motion capture, sidestepping, sports biomechanics
MeSH terms
Anterior Cruciate Ligament Injuries, Risk Evaluation and Mitigation, Deep Learning, Biomechanical Phenomena, Female, Humans, Knee Injuries, Male, Motion, Movement, Risk Factors, Sports, Workload, Neural Networks, Computer, Athletes

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