Many of the available gait monitoring technologies are expensive, require specialized expertise, are time consuming to use, and are not widely available for clinical use. The advent of video-based pose tracking provides an opportunity for inexpensive automated analysis of human walking in older adults using video cameras. However, there is a need to validate gait parameters calculated by these algorithms against gold standard methods for measuring human gait data in this population. We compared quantitative gait variables of 11 older adults (mean age = 85.2) calculated from video recordings using three pose trackers (AlphaPose, OpenPose, Detectron) to those calculated from a 3D motion capture system. We performed comparisons for videos captured by two cameras at two different viewing angles, and viewed from the front or back. We also analyzed the data when including gait variables of individual steps of each participant or each participant’s averaged gait variables. Our findings revealed that, i) temporal (cadence and step time), but not spatial and variability gait measures (step width, estimated margin of stability, coefficient of variation of step time and width), calculated from the video pose tracking algorithms correlate significantly to that of motion capture system, and ii) there are minimal differences between the two camera heights, and walks viewed from the front or back in terms of correlation of gait variables, and iii) gait variables extracted from AlphaPose and Detectron had the highest agreement while OpenPose had the lowest agreement. There are important opportunities to evaluate models capable of 3D pose estimation in video data, improve the training of pose-tracking algorithms for older adult and clinical populations, and develop video-based 3D pose trackers specifically optimized for quantitative gait measurement.

中文翻译：

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用于测量老年人步态参数的视频中人体姿势跟踪的并发有效性：对多个跟踪器、视角和步行方向的初步分析

许多可用的步态监测技术价格昂贵，需要专业知识，使用起来很费时间，并且不能广泛用于临床。基于视频的姿势跟踪的出现为使用摄像机对老年人的步行进行廉价的自动分析提供了机会。但是，需要根据用于测量该人群中人类步态数据的黄金标准方法来验证这些算法计算出的步态参数。我们将使用三个姿势跟踪器（AlphaPose、OpenPose、Detectron）从视频记录中计算出的 11 位老年人（平均年龄 = 85.2）的定量步态变量与从 3D 运动捕捉系统计算出的步态变量进行了比较。我们对两个摄像头在两个不同视角拍摄的视频进行了比较，并从正面或背面观看。我们还分析了包括每个参与者的各个步骤的步态变量或每个参与者的平均步态变量时的数据。我们的研究结果表明，i) 时间（节奏和步进时间），但不是空间和可变性步态测量（步宽、估计的稳定性裕度、步进时间和宽度的变异系数），从视频姿势跟踪算法计算出显着相关与动作捕捉系统的差异，以及 ii) 两个相机高度之间的差异很小，并且在步态变量的相关性方面，从前面或后面看步行，以及 iii) 从 AlphaPose 和 Detectron 中提取的步态变量具有最高的一致性而 OpenPose 的一致性最低。有重要的机会来评估能够在视频数据中进行 3D 姿态估计的模型，