Background

We study dual-task performance with marker-free video recordings of Timed Up-and-Go tests (TUG) and TUG combined with a cognitive/verbal task (TUG dual-task, TUGdt).

Research question

Can gait parameters be accurately estimated from video-recorded TUG tests by a new semi-automatic method aided by a technique for human 2D pose estimation based on deep learning?

Methods

Thirty persons aged 60−85 years participated in the study, conducted in a laboratory environment. Data were collected by two synchronous video-cameras and a marker-based optoelectronic motion capture system as gold standard, to evaluate the gait parameters step length (SL), step width (SW), step duration (SD), single-stance duration (SSD) and double-stance duration (DSD). For reliability evaluations, data processing aided by a deep neural network model, involved three raters who conducted three repetitions of identifying anatomical keypoints in recordings of one randomly selected step from each of the participants. Validity was analysed using 95 % confidence intervals (CI) and p-values for method differences and Bland-Altman plots with limits of agreement. Inter- and intra-rater reliability were calculated as intraclass correlation coefficients (ICC) and standard errors of measurement. Smallest detectable change was calculated for inter-rater reliability.

Results

Mean ddifferences between video and the motion capture system data for SW, DSD, and SSD were significant (p < 0.001). However, mean differences for all parameters were small (-6.4%–13.0% of motion capture system) indicating good validity. Concerning reliability, almost all 95 % CI of the ICC estimates exceeded 0.90, indicating excellent reliability. Only inter-rater reliability for SW (95 % CI = 0.892;0.973) and one rater’s intra-rater reliability for SSD (95 % CI = 0.793;0.951) were lower, but still showed good to excellent reliability.

Significance

The presented method for extraction of gait parameters from video appears suitable for valid and reliable quantification of gait. This opens up for analyses that may contribute to the knowledge of cognitive-motor interference in dual-task testing.

中文翻译：

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从 Timed Up-and-Go 测试的无标记视频记录中提取步态参数：有效性、评分者间和评分者内的可靠性

背景

我们通过定时启动测试 (TUG) 和 TUG 结合认知/语言任务 (TUG 双任务，TUGdt) 的无标记视频记录来研究双任务性能。

研究问题

在基于深度学习的人体二维姿态估计技术的辅助下，能否通过一种新的半自动方法从视频录制的 TUG 测试中准确估计步态参数？

方法

30 名 60-85 岁的人参加了在实验室环境中进行的研究。通过两个同步摄像机和基于标记的光电运动捕捉系统作为黄金标准收集数据，以评估步态参数步长 (SL)、步宽 (SW)、步长 (SD)、单站时长 ( SSD) 和双站姿持续时间 (DSD)。对于可靠性评估，由深度神经网络模型辅助的数据处理涉及三个评估者，他们在每个参与者的一个随机选择的步骤的记录中重复识别解剖关键点的三个重复。使用 95% 置信区间 (CI) 和方法差异的 p 值以及具有一致性限制的 Bland-Altman 图分析有效性。评估者间和评估者内的可靠性计算为组内相关系数 (ICC) 和测量的标准误差。为评估者间的可靠性计算了最小的可检测变化。

结果

视频与 SW、DSD 和 SSD 的运动捕捉系统数据之间的平均差异显着（p < 0.001）。然而，所有参数的平均差异很小（运动捕捉系统的-6.4%–13.0%），表明有效性良好。关于可靠性，ICC 估计的几乎所有 95% CI 都超过了 0.90，表明可靠性极好。只有 SW 的评估者间信度 (95% CI = 0.892;0.973) 和一名评估者的 SSD 的评估者内信度 (95% CI = 0.793;0.951) 较低，但仍表现出良好至极好的信度。

意义

所提出的从视频中提取步态参数的方法似乎适用于步态的有效和可靠量化。这为可能有助于了解双任务测试中认知运动干扰知识的分析打开了大门。