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David, S and Barton, GJ (2024) Characterization of movement patterns using unsupervised learning neural networks: Exploring a novel approach for monitoring athletes during sidestepping. Journal of Sports Sciences, 41 (20). pp. 1845-1851. ISSN 0264-0414
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Characterization of movement patterns using unsupervised learning neural networks Exploring a novel approach for monitoring athletes during sidestepping.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (7MB) | Preview |
Abstract
The monitoring of athletes is crucial to preventing injuries, identifying fatigue or supporting return-to-play decisions. The purpose of this study was to explore the ability of Kohonen neural network self-organizing maps (SOM) to objectively characterize movement patterns during sidestepping and their association with injury risk. Further, the network’s sensitivity to detect limb dominance was assessed. The data of 67 athletes with a total of 613 trials were included in this study. The 3D trajectories of 28 lower-body passive markers collected during sidestepping were used to train a SOM. The network consisted of 1247 neurons distributed over a 43 × 29 rectangular map with a hexagonal neighbourhood topology. Out of 61,913 input vectors, the SOM identified 1247 unique body postures. Visualizing the movement trajectories and adding several hidden variables allows for the investigation of different movement patterns and their association with joint loading. The used approach identified athletes that show significantly different movement strategies when sidestepping with their dominant or non-dominant leg, where one strategy was clearly associated with ACL-injury-relevant risk factors. The results highlight the ability of unsupervised machine learning to monitor an individual athlete’s status without the necessity to reduce the complexity of the data describing the movement.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Knee Joint; Humans; Movement; Athletes; Biomechanical Phenomena; Unsupervised Machine Learning; Anterior Cruciate Ligament Injuries; Neural Networks, Computer; Screening; data-reduction; injury risk; machine learning; return-to-play; self-organizing maps; Humans; Knee Joint; Unsupervised Machine Learning; Neural Networks, Computer; Movement; Athletes; Anterior Cruciate Ligament Injuries; Biomechanical Phenomena; 1106 Human Movement and Sports Sciences; 1302 Curriculum and Pedagogy; Sport Sciences |
| Subjects: | R Medicine > RC Internal medicine > RC1200 Sports Medicine |
| Divisions: | Sport & Exercise Sciences |
| Publisher: | Taylor and Francis Group |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 11 Sep 2024 16:18 |
| Last Modified: | 11 Sep 2024 16:30 |
| DOI or ID number: | 10.1080/02640414.2023.2300570 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/24122 |
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