Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan.
Department of Physical Therapy, Faculty of Health Sciences, Hokkaido University of Science, Sapporo, Japan.
J Sports Sci Med. 2024 Sep 1;23(1):515-525. doi: 10.52082/jssm.2024.515. eCollection 2024 Sep.
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.
基于 OpenPose 的运动分析(OpenPose-MA),利用深度学习方法,已成为估计人体运动的一种极具吸引力的技术。它解决了传统的三维运动分析(3D-MA)和基于人体视觉检测的运动分析(Human-MA)的缺陷,包括昂贵的设备、耗时的分析和受限的实验设置。本研究旨在评估 OpenPose-MA 与 Human-MA 的精确性,以 3D-MA 为参考标准。研究涉及 21 名年轻健康的成年人。OpenPose-MA 使用 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。Human-MA 由一名熟练的物理治疗师进行。通过 OpenPose-MA 和 Human-MA 使用相同的正面视频图像计算在垂直跳下落任务中的膝关节外翻角度,以 3D-MA 作为参考标准。利用多种指标评估不同方法之间膝关节外翻角度的可重复性、准确性和相似性,包括组内相关系数(ICC)(1、3)、平均绝对误差(MAE)、波形模式相似的多相关系数(CMC)和 Pearson 相关系数(OpenPose-MA 与 3D-MA,Human-MA 与 3D-MA)。进行了独立样本 t 检验以比较 OpenPose-MA 和 Human-MA 之间的 MAE 和 CMC。在 DVJ 试验中,OpenPose-MA、Human-MA 和 3D-MA 的 ICC(1、3)显示出极好的可重复性。在膝关节外翻角度的 MAE(OpenPose:2.4°[95%CI:1.9-3.0°],Human:3.2°[95%CI:2.1-4.4°])或 CMC(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 之间没有显著差异。OpenPose-MA 和 Human-MA 与 3D-MA 的 Pearson 相关系数分别为 0.97 和 0.98。本研究表明,OpenPose-MA 达到了令人满意的可重复性、准确性,并表现出与 3D-MA 相似的波形相似性,与 Human-MA 相似。在膝关节外翻角度位移方面,OpenPose-MA 和 Human-MA 均与 3D-MA 表现出很强的相关性。