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Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury
This study developed a method to detect knee wobbling (KW) at low knee flexion. KW consists of quick uncontrollable medio-lateral knee movements without knee flexion, which may indicate a risk of ACL injury. Ten female athletes were recorded while performing slow, single-leg squats. Using motion cap...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330762/ https://www.ncbi.nlm.nih.gov/pubmed/34338140 http://dx.doi.org/10.1080/23335432.2021.1936175 |
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author | Aoki, Akino Kubota, Satoshi Morinaga, Kosuke Zheng, Naiquan Nigel Wang, Shangcheng Sam Gamada, Kazuyoshi |
author_facet | Aoki, Akino Kubota, Satoshi Morinaga, Kosuke Zheng, Naiquan Nigel Wang, Shangcheng Sam Gamada, Kazuyoshi |
author_sort | Aoki, Akino |
collection | PubMed |
description | This study developed a method to detect knee wobbling (KW) at low knee flexion. KW consists of quick uncontrollable medio-lateral knee movements without knee flexion, which may indicate a risk of ACL injury. Ten female athletes were recorded while performing slow, single-leg squats. Using motion capture data, the ratio of the frontal angular velocity to sagittal angular velocity (F/S) was calculated. An ‘F/S spike’ was defined when the F/S ratio exceeded 100%. The number of F/S spikes was counted before and after low-pass filtering at different cut-off frequencies. Intraclass correlation coefficients for KW and filtered F/S spike were analysed. KWs per squat cycle showed a median (range) of 3 (2–8) times. F/S spikes before and after low-pass filtering at 3-, 6-, 10-, and 15-Hz were 51 (12–108), 2 (0–6), 3 (1–12), 5 (2–21), and 9 (3–33) times, respectively. KWs and F/S spikes on motion capture with 6-Hz, low-pass filtering were well correlated (r = 0 .76). Median percentages of valgus and varus F/S spikes were 71% and 29%, respectively. After 6Hz, low-pass filtering, the number of F/S spikes was strongly correlated with observed KWs. An F/S spike assessment may be used to objectively detect KW, including flexion and varus/valgus angular velocity. |
format | Online Article Text |
id | pubmed-8330762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-83307622021-08-09 Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury Aoki, Akino Kubota, Satoshi Morinaga, Kosuke Zheng, Naiquan Nigel Wang, Shangcheng Sam Gamada, Kazuyoshi Int Biomech Article This study developed a method to detect knee wobbling (KW) at low knee flexion. KW consists of quick uncontrollable medio-lateral knee movements without knee flexion, which may indicate a risk of ACL injury. Ten female athletes were recorded while performing slow, single-leg squats. Using motion capture data, the ratio of the frontal angular velocity to sagittal angular velocity (F/S) was calculated. An ‘F/S spike’ was defined when the F/S ratio exceeded 100%. The number of F/S spikes was counted before and after low-pass filtering at different cut-off frequencies. Intraclass correlation coefficients for KW and filtered F/S spike were analysed. KWs per squat cycle showed a median (range) of 3 (2–8) times. F/S spikes before and after low-pass filtering at 3-, 6-, 10-, and 15-Hz were 51 (12–108), 2 (0–6), 3 (1–12), 5 (2–21), and 9 (3–33) times, respectively. KWs and F/S spikes on motion capture with 6-Hz, low-pass filtering were well correlated (r = 0 .76). Median percentages of valgus and varus F/S spikes were 71% and 29%, respectively. After 6Hz, low-pass filtering, the number of F/S spikes was strongly correlated with observed KWs. An F/S spike assessment may be used to objectively detect KW, including flexion and varus/valgus angular velocity. Taylor & Francis 2021-08-02 /pmc/articles/PMC8330762/ /pubmed/34338140 http://dx.doi.org/10.1080/23335432.2021.1936175 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Article Aoki, Akino Kubota, Satoshi Morinaga, Kosuke Zheng, Naiquan Nigel Wang, Shangcheng Sam Gamada, Kazuyoshi Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury |
title | Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury |
title_full | Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury |
title_fullStr | Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury |
title_full_unstemmed | Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury |
title_short | Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury |
title_sort | detection of knee wobbling as a screen to identify athletes who may be at high risk for acl injury |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330762/ https://www.ncbi.nlm.nih.gov/pubmed/34338140 http://dx.doi.org/10.1080/23335432.2021.1936175 |
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