School of Health Professions, University of Missouri, Columbia, MO, USA.
Department of Physical Therapy, University of Missouri, Columbia, MO, USA.
Gait Posture. 2022 May;94:153-159. doi: 10.1016/j.gaitpost.2022.03.011. Epub 2022 Mar 21.
The sit-to-stand test (STS) is commonly used to evaluate functional capabilities within a variety of clinical populations. Traditionally STS is a timed test, limiting the depth of information which can be gained from its evaluation. The Azure Kinect has the potential to add in-depth analysis to STS. Despite these potential benefits, the recently released (2019) Azure Kinect has yet to be evaluated for its ability to accurately assess STS.
Purposes of this work were to compare data captured during STS using both a 12 camera Vicon motion capture system and the Azure Kinect; and to calculate kinematic and spatiotemporal variables related to the four phases of the STS cycle.
Spatiotemporal and kinematic measures for STS were simultaneously collected by both devices for 15 participants. Cycle waveforms were compared for right and left hip and knee flexion/extension angular displacement, right and left hip and knee flexion/extension angular velocity, and knee-to-ankle separation ratio. Evaluated discrete outcome variables included: phase time points, maximum knee extension velocity from phases 3 to 4, medial-lateral pelvic sway range, and total time to completion. Waveform summary data were compared using R, R, and RMSE. Discrete variables were analyzed using Spearman's Rank correlation coefficient.
R and R values between the two systems indicated high levels of correlation (all R values > 0.711, all R values > 0.660). Although there was an overall high level of agreement between waveform shapes, high RMSE values indicated some minor tracking errors for Kinect within the STS cycle. Spearman's Rank correlation coefficient indicated high levels of correlation between the systems for discrete variables (all R values > 0.89), with the exception of medial-lateral pelvic sway range.
The Azure Kinect provides valuable insight into STS movement strategies allowing for improved precision in clinical decision making across multiple clinical populations.
坐站测试（STS）常用于评估各种临床人群的功能能力。传统上，STS 是一项计时测试，限制了从其评估中获得的信息深度。Azure Kinect 有可能为 STS 提供深入的分析。尽管有这些潜在的好处，但最近发布的（2019 年）Azure Kinect 尚未评估其准确评估 STS 的能力。
本研究的目的是比较使用 12 个摄像头 Vicon 运动捕捉系统和 Azure Kinect 采集的 STS 期间的数据；并计算与 STS 周期四个阶段相关的运动学和时空变量。
15 名参与者同时使用两种设备同步采集 STS 的时空和运动学测量值。比较了右侧和左侧髋关节和膝关节屈伸角位移、右侧和左侧髋关节和膝关节屈伸角速度以及膝关节到踝关节的分离比的循环波形。评估的离散结果变量包括：阶段时间点、从阶段 3 到 4 的最大膝关节伸展速度、骨盆左右摆动范围和完成总时间。使用 R、R 和 RMSE 比较了波形汇总数据。使用 Spearman 秩相关系数分析了离散变量。
两种系统之间的 R 和 R 值表明高度相关（所有 R 值>0.711，所有 R 值>0.660）。尽管两种波形形状总体上高度一致，但高 RMSE 值表明 Kinect 在 STS 周期内存在一些轻微的跟踪误差。Spearman 秩相关系数表明两种系统在离散变量之间具有高度相关性（所有 R 值>0.89），除了骨盆左右摆动范围。
Azure Kinect 为 STS 运动策略提供了有价值的见解，为多个临床人群的临床决策提供了更高的精度。
