Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.

IF 2.4 2区 医学 Q2 SPORT SCIENCES Journal of Sports Science and Medicine Pub Date : 2024-09-01 DOI:10.52082/jssm.2024.515
Takumi Ino, Mina Samukawa, Tomoya Ishida, Naofumi Wada, Yuta Koshino, Satoshi Kasahara, Harukazu Tohyama
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Abstract

OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and human visual detection-based motion analysis (Human-MA), including costly equipment, time-consuming analysis, and restricted experimental settings. This study aims to assess the precision of OpenPose-MA in comparison to Human-MA, using 3D-MA as the reference standard. The study involved a cohort of 21 young and healthy adults. OpenPose-MA employed the OpenPose algorithm, a deep learning-based open-source two-dimensional (2D) pose estimation method. Human-MA was conducted by a skilled physiotherapist. The knee valgus angle during a drop vertical jump task was computed by OpenPose-MA and Human-MA using the same frontal-plane video image, with 3D-MA serving as the reference standard. Various metrics were utilized to assess the reproducibility, accuracy and similarity of the knee valgus angle between the different methods, including the intraclass correlation coefficient (ICC) (1, 3), mean absolute error (MAE), coefficient of multiple correlation (CMC) for waveform pattern similarity, and Pearson's correlation coefficients (OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA). Unpaired t-tests were conducted to compare MAEs and CMCs between OpenPose-MA and Human-MA. The ICCs (1,3) for OpenPose-MA, Human-MA, and 3D-MA demonstrated excellent reproducibility in the DVJ trial. No significant difference between OpenPose-MA and Human-MA was observed in terms of the MAEs (OpenPose: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) or CMCs (OpenPose: 0.83 [range: 0.99-0.53], Human: 0.87 [range: 0.24-0.98]) of knee valgus angles. The Pearson's correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively. This study demonstrated that OpenPose-MA achieved satisfactory reproducibility, accuracy and exhibited waveform similarity comparable to 3D-MA, similar to Human-MA. Both OpenPose-MA and Human-MA showed a strong correlation with 3D-MA in terms of knee valgus angle excursion.

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基于 OpenPose 的运动分析法在测量垂足立定跳远测试中膝内翻的有效性和可靠性
基于 OpenPose 的运动分析(OpenPose-MA)利用深度学习方法,已成为估算人体运动的一项引人注目的技术。它解决了传统三维运动分析(3D-MA)和基于人类视觉检测的运动分析(Human-MA)的相关缺点,包括昂贵的设备、耗时的分析和有限的实验设置。本研究旨在以三维运动分析为参考标准,评估 OpenPose-MA 与 Human-MA 相比的精确度。研究涉及 21 名年轻健康的成年人。OpenPose-MA 采用了 OpenPose 算法,这是一种基于深度学习的开源二维(2D)姿势估计方法。人体姿势评估由一名熟练的理疗师进行。OpenPose-MA和Human-MA使用相同的额面视频图像计算落体垂直跳跃任务中的膝外翻角度,并将3D-MA作为参考标准。利用各种指标来评估不同方法之间膝外翻角度的重现性、准确性和相似性,包括类内相关系数(ICC)(1, 3)、平均绝对误差(MAE)、波形模式相似性的多重相关系数(CMC)和皮尔逊相关系数(OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA )。对 OpenPose-MA 和 Human-MA 之间的 MAE 和 CMC 进行了非配对 t 检验。在 DVJ 试验中,OpenPose-MA、Human-MA 和 3D-MA 的 ICCs (1,3) 均显示出极佳的重现性。在膝关节外翻角度的 MAEs(OpenPose:2.4° [95%CI:1.9-3.0°],Human:3.2° [95%CI:2.1-4.4°])或 CMCs(OpenPose:0.83 [范围:0.99-0.53],Human:0.87 [范围:0.24-0.98])方面,OpenPose-MA 和 Human-MA 之间没有观察到明显差异。与 3D-MA 相比,OpenPose-MA 和人体-MA 的皮尔逊相关系数分别为 0.97 和 0.98。这项研究表明,OpenPose-MA 的再现性和准确性令人满意,其波形相似性与 3D-MA 相当,与人体-MA 相似。在膝外翻角偏移方面,OpenPose-MA 和人体-MA 与 3D-MA 都显示出很强的相关性。
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来源期刊
CiteScore
5.60
自引率
6.20%
发文量
56
审稿时长
4-8 weeks
期刊介绍: The Journal of Sports Science and Medicine (JSSM) is a non-profit making scientific electronic journal, publishing research and review articles, together with case studies, in the fields of sports medicine and the exercise sciences. JSSM is published quarterly in March, June, September and December. JSSM also publishes editorials, a "letter to the editor" section, abstracts from international and national congresses, panel meetings, conferences and symposia, and can function as an open discussion forum on significant issues of current interest.
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