| Autor: |
Makoto Asaeda, Tomoya Onishi, Hideyuki Ito, So Miyahara, Yukio Mikami |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Heliyon, Vol 10, Iss 17, Pp e36338- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2405-8440 |
| DOI: |
10.1016/j.heliyon.2024.e36338 |
| Popis: |
Background: The consensus on anterior cruciate ligament (ACL) injury prevention involves the suppression of dynamic knee valgus (DKV). The gold standard for evaluating the DKV includes three-dimensional motion analysis systems; however, these are expensive and cannot be used to evaluate all athletes. Markerless motion-capture systems and joint angle calculations using posture estimation have been reported. However, there have been no reports on the reliability and validity of DKV calculations using posture estimation. Research question: This study aimed to clarify the reliability and validity of DKV calculation using posture estimation. Methods: Fifteen participants performed 10 single-leg jump landings from a height of 20 cm, and the knee joint angle was calculated using joint points measured using machine learning (MediaPipe Pose) and motion-capture systems (VICON MX). Two types of angle calculation methods were used: absolute value and change from the initial ground contact (IC). Intra- and inter-rater reliabilities were examined using intraclass correlation coefficients, and concurrent validity was examined using Pearson's correlation coefficients. To examine intra-examiner reliability, we performed single-leg jump landings at intervals of ≥3 days. Results: The calculation by MediaPipe Pose was significantly higher than that by the 3-D motion analysis systems (p 0.05). No significant concurrent validity was found in the absolute value, which was significantly correlated with the change in IC. Although the inter-rater reliability of the absolute value was low, the change in IC showed good reliability and concurrent validity. Significance: The results of this research suggest that the DKV calculation by pose estimation using machine learning is practical, with normalization by the angle at IC. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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