ArticleHeliyon2024
Reliability and validity of knee valgus angle calculation at single-leg drop landing by posture estimation using machine learning.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy and Validity of 3D Markerless Motion Capture Compared to Marker-Based Systems for Lower-Limb Biomechanical Assessment: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Artificial intelligence in ACL injury prediction and prevention: a systematic review.Journal of orthopaedic surgery and research · 2026Pooled it
- Markerless Motion Capture for Human Movement Estimation Using Artificial Intelligence: A Systematic Review.Pediatric reports · 2026Review
- Comparative Analysis of Markerless Motion-Capture Models for Assessing Football Kinematics During 30 m Long-Pass Tasks.Sensors (Basel, Switzerland) · 2026Article
- Assessment of dynamic knee angle deviations in the frontal plane in physiotherapy clinical practice: intra- and inter-rater reliability of an application and agreement with two AI-models.BMC musculoskeletal disorders · 2026Article
- Feasibility of explainable machine learning for analyzing drop landing strategies: effects of landing height and fatigue.Frontiers in physiology · 2026Article
- Evaluation of the Impact of External Conditions on Arm Positioning During Punches in MMA Fighters: A Comparative Analysis of 2D and 3D Methods.Sensors (Basel, Switzerland) · 2025Article
- Between-Day Reliability of Kinematic Variables Using Markerless Motion Capture for Single-Leg Squat and Single-Leg Landing Tasks.International journal of sports physical therapy · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, error range 18.83-19.68°), and there was no main effect of knee valgus angle or time on the excursion angle from IC (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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.