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使用 Azure Kinect 测量障碍物穿越过程中的足部间隙:验证研究。

Use of the Azure Kinect to measure foot clearance during obstacle crossing: A validation study.

机构信息

Graduate School of Humanities and Social Science, Hiroshima University, Higashi-Hiroshima, Japan.

出版信息

PLoS One. 2022 Mar 11;17(3):e0265215. doi: 10.1371/journal.pone.0265215. eCollection 2022.

Abstract

Obstacle crossing is typical adaptive locomotion known to be related to the risk of falls. Previous conventional studies have used elaborate and costly optical motion capture systems, which not only represent a considerable expense but also require participants to visit a laboratory. To overcome these shortcomings, we aimed to develop a practical and inexpensive solution for measuring obstacle-crossing behavior by using the Microsoft Azure Kinect, one of the most promising markerless motion capture systems. We validated the Azure Kinect as a tool to measure foot clearance and compared its performance to that of an optical motion capture system (Qualisys). We also determined the effect of the Kinect sensor placement on measurement performance. Sixteen healthy young men crossed obstacles of different heights (50, 150, and 250 mm). Kinect sensors were placed in front of and beside the obstacle as well as diagonally between those positions. As indices of measurement quality, we counted the number of measurement failures and calculated the systematic and random errors between the foot clearance measured by the Kinect and Qualisys. We also calculated the Pearson correlation coefficients between the Kinect and Qualisys measurements. The number of measurement failures and the systematic and random error were minimized when the Kinect was placed diagonally in front of the obstacle on the same side as the trail limb. The high correlation coefficient (r > 0.890) observed between the Kinect and Qualisys measurements suggest that the Azure Kinect has excellent potential for measuring foot clearance during obstacle-crossing tasks.

摘要

跨越障碍物是一种典型的适应性运动,已知与跌倒风险有关。以前的传统研究使用了复杂且昂贵的光学运动捕捉系统,这不仅代表了相当大的费用,而且还要求参与者前往实验室。为了克服这些缺点,我们旨在使用最有前途的无标记运动捕捉系统之一 Microsoft Azure Kinect,开发一种用于测量跨越障碍物行为的实用且廉价的解决方案。我们验证了 Azure Kinect 作为测量足间隙的工具,并将其性能与光学运动捕捉系统(Qualisys)进行了比较。我们还确定了 Kinect 传感器放置对测量性能的影响。十六名健康的年轻男性跨越了不同高度(50、150 和 250 毫米)的障碍物。Kinect 传感器放置在障碍物的前面和旁边,以及这些位置之间的对角线位置。作为测量质量的指标,我们计算了测量失败的次数,并计算了 Kinect 和 Qualisys 测量的足间隙之间的系统和随机误差。我们还计算了 Kinect 和 Qualisys 测量之间的 Pearson 相关系数。当 Kinect 以与试验肢体相同的一侧对角线放置在障碍物的前面时,测量失败的次数以及系统和随机误差最小。Kinect 和 Qualisys 测量之间观察到的高相关系数(r > 0.890)表明 Azure Kinect 在测量跨越障碍物任务中的足间隙方面具有出色的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca40/8916621/fcda6f6ab542/pone.0265215.g001.jpg

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