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基于智能手机的无标记三维运动捕捉的可重复性和最小可检测变化,包括服装效应。

Repeatability and minimal detectable change including clothing effects for smartphone-based 3D markerless motion capture.

机构信息

Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria; Institute of Health Sciences, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria.

Centre for Sport Science and University Sports, Department of Biomechanics, Kinesiology, and Computer Science in Sport, Neuromechanics Research Group, University of Vienna, Auf der Schmelz 6a, Vienna, 1150, Austria.

出版信息

J Biomech. 2024 Oct;175:112281. doi: 10.1016/j.jbiomech.2024.112281. Epub 2024 Aug 13.

Abstract

OpenCap, a smartphone- and web-based markerless system, has shown acceptable accuracy compared to marker-based systems, but lacks information on repeatability. This study fills this gap by evaluating the intersession repeatability of OpenCap and investigating the effects of clothing on gait kinematics. Twenty healthy volunteers participated in a test-retest study, performing walking and sit-to-stand tasks with minimal clothing and regular street wear. Segment lengths and lower-limb kinematics were compared between both sessions and for both clothing conditions using the root-mean-square-deviation (RMSD) for entire waveforms and the standard error of measurement (SEM) and minimal detectable change (MDC) for discrete kinematic parameters. In general, the RMSD test-retest values were 2.8 degrees (SD: 1.0) for walking and 3.3 degrees (SD: 1.2) for sit-to-stand. The highest intersession variability was observed in the trunk, pelvis, and hip kinematics of the sagittal plane. SEM and MDC values were on average 2.2 and 6.0 degrees, respectively, for walking, and 2.4 and 6.5 degrees for sit-to-stand. Clothing had minimal effects on kinematics by adding on average less than one degree to the RMSD values for most variables. The segment lengths showed moderate to excellent agreement between both sessions and poor to moderate agreement between clothing conditions. The study highlights the reliability of OpenCap for markerless motion capture, emphasizing its potential for large-scale field studies. However, some variables showed high MDC values above 5 degrees and thus warrant further enhancement of the technology. Although clothing had minimal effects, it is still recommended to maintain consistent clothing to minimize overall variability.

摘要

OpenCap 是一种基于智能手机和网络的无标记系统,与基于标记的系统相比,其准确性已得到验证,但缺乏关于重复性的信息。本研究通过评估 OpenCap 的会话间重复性并研究服装对步态运动学的影响,填补了这一空白。20 名健康志愿者参与了一项测试-再测试研究,在穿着最少的衣服和日常穿着的情况下进行行走和坐-站任务。使用整个波形的均方根偏差(RMSD)和离散运动学参数的测量误差(SEM)和最小可检测变化(MDC),比较了两次测试之间以及两种服装条件下的节段长度和下肢运动学。通常,行走时的 RMSD 测试-再测试值为 2.8 度(SD:1.0),坐-站时为 3.3 度(SD:1.2)。矢状面躯干、骨盆和髋关节运动学的会话间变异性最大。SEM 和 MDC 值分别为行走时的平均 2.2 度和 6.0 度,坐-站时为 2.4 度和 6.5 度。服装对运动学的影响很小,平均为 RMSD 值增加不到 1 度。节段长度在两次测试之间具有良好的一致性,在服装条件之间具有较差的一致性。该研究突出了 OpenCap 用于无标记运动捕捉的可靠性,强调了其在大规模现场研究中的潜力。然而,一些变量的 MDC 值高于 5 度,因此需要进一步改进技术。尽管服装的影响很小,但仍建议保持一致的服装以最小化整体变异性。

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