On-field player workload exposure and knee injury risk monitoring via deep learning
Open accessAbstract
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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CNN extracts local spatial features and uses pooling layers for dimensionality reduction, efficiently captures spatial correlations in multi-channel signals (e.g., sEMG), and is used for sEMG gesture classification and IMU-based gait feature extraction.
“| CNN | Extract local spatial features, pooling layers for | Efficiently capture spatial correlations in multi- | sEMG gesture classification, IMU-based gait |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [247]
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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
- bronze
- doi.org
- 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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