Department of Physical Therapy, University of Missouri, Columbia, MO, USA; Department of Orthopaedic Surgery, University of Missouri, Columbia, MO, USA.
Department of Physical Therapy, University of Missouri, Columbia, MO, USA.
Gait Posture. 2022 Jul;96:130-136. doi: 10.1016/j.gaitpost.2022.05.021. Epub 2022 May 21.
Instrumented measurement of spatiotemporal parameters during walking can provide valuable information on an individual's overall function and health. Efficient, inexpensive, and accurate measurement of overground walking spatiotemporal parameters would be a critical component of providing point-of-care assessments of gait function, concussion recovery, fall-risk, and cognitive decline. Depth cameras combined with skeleton pose tracking algorithms, such as the Microsoft Kinect with body tracking software, have been used to measure walking spatiotemporal parameters. However, the ability of the latest generation Microsoft Kinect sensor, the Azure Kinect, to accurately measure overground walking spatiotemporal parameters has not been evaluated in the literature.
The purpose of this work was to compare overground walking spatiotemporal parameters measurements from a 12 camera Vicon optical motion capture system to measurements of a single Azure Kinect with body tracking SDK (software development kit).
Spatiotemporal parameters of overground walking were simultaneously collected on twenty young healthy participants. Stride length, stride time, step length and step width were derived from ankle joint center locations and measurements from the two instruments were compared using descriptive statistics, scatter plots, Pearson correlation analyses, and Bland-Altman analyses.
Pearson correlation coefficients were greater than 0.87 for all spatiotemporal parameters with most parameters demonstrating very strong (> 0.9) agreement. The mean of the differences for stride length between measurements was 35.6 mm for the left limb and 39.1 mm for the right limb, both of which are less than 3% of average stride length. Mean of the differences for step width and stride time were less than 2% and 1% of their averages respectively.
A single Microsoft Azure Kinect with body tracking SDK can provide clinically relevant measurement of walking spatiotemporal parameters, providing accessible and objective measurements that can improve clinical decision making across a variety of patient populations.
仪器测量行走过程中的时空参数可以提供个体整体功能和健康状况的有价值信息。高效、廉价且准确地测量地面行走时空参数将是提供即时步态功能评估、脑震荡恢复、跌倒风险和认知能力下降评估的关键组成部分。深度摄像机与骨骼姿势跟踪算法（如带有身体跟踪软件的 Microsoft Kinect）结合使用，已经被用于测量行走时空参数。然而，最新一代 Microsoft Kinect 传感器（Azure Kinect）准确测量地面行走时空参数的能力尚未在文献中得到评估。
本研究的目的是比较使用 12 个摄像机 Vicon 光学运动捕捉系统和单个 Azure Kinect 与身体跟踪 SDK（软件开发套件）测量的地面行走时空参数。
同时收集 20 名年轻健康参与者的地面行走时空参数。步长、步幅时间、步长和步宽是根据踝关节中心位置和来自两个仪器的测量值计算得出的，使用描述性统计、散点图、皮尔逊相关分析和 Bland-Altman 分析比较两种测量方法的结果。
所有时空参数的皮尔逊相关系数均大于 0.87，大多数参数显示出非常强的（>0.9）一致性。左右腿的步长测量值之间的差异平均值分别为 35.6mm 和 39.1mm，均小于平均步长的 3%。步宽和步幅时间的差异平均值分别小于其平均值的 2%和 1%。
单个 Microsoft Azure Kinect 与身体跟踪 SDK 可以提供具有临床意义的行走时空参数测量，提供易于获取和客观的测量结果，可以改善各种患者群体的临床决策。
