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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...

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Autores principales: Aoki, Akino, Kubota, Satoshi, Morinaga, Kosuke, Zheng, Naiquan Nigel, Wang, Shangcheng Sam, Gamada, Kazuyoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
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.
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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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