Anderson Jeffrey T, Stenum Jan, Roemmich Ryan T, Wilson Rujuta B
Department of Medicine, University of California, Los Angeles, CA, United States.
Department of Physical Medicine and Rehabilitation, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Front Digit Health. 2025 Feb 25;7:1542012. doi: 10.3389/fdgth.2025.1542012. eCollection 2025.
The onset of locomotion is a critical motor milestone in early childhood and increases engagement with the environment. Toddlers with neurodevelopmental disabilities often have atypical motor development that impacts later outcomes. Video-based gait analysis using pose estimation offers an alternative to standardized motor assessments which are subjective and difficult to ascertain in some populations, yet very little work has been done to determine its accuracy in young children. To fill this gap, this study aims to assess the feasibility and accuracy of pose estimation for gait analysis in children with a range of developmental levels.
We analyzed the overground gait of 112 toddlers (M: 30 months, SD: 8 months) with and without developmental disabilities using the ProtoKinetics Zeno Walkway system. Simultaneously recorded videos were processed in OpenPose to perform pose estimation and a custom MATLAB workflow to calculate average spatiotemporal gait parameters. Pearson correlations were used to compare OpenPose with the Zeno Walkway for velocity, step length, and step time. A Bland-Altman analysis (difference vs. average) was used to assess the agreement between methodologies and determine the difference of means. Developmental levels were assessed using the Mullen Scales of Early Learning.
Our analysis included children with autism ( = 77), non-autism developmental concerns ( = 6), tuberous sclerosis complex ( = 13), 22q deletion ( = 1), and typical development ( = 15). Mullen early learning composite scores ranged from 49 to 95 (m = 80.91, sd = 26.68). Velocity (r = 0.87, < 0.0001), step length (r = 0.79, < 0.0001), and step time (r = 0.96, < 0.0001) were all highly correlated between OpenPose and the Zeno Walkway, with an absolute difference of means of 0.04 m/s, 0.03 m, and 0.01 s, respectively.
Our results suggest that video-based gait analysis using pose estimation is accurate in toddlers with a range of developmental levels. Video-based gait analysis is low cost and can be implemented for remote data collection in natural environments such as a participant's home. These advantages open possibilities for using repeated measures to increase our knowledge of how gait ability changes over time in pediatric populations and improve clinical screening tools, particularly in those with neurodevelopmental disabilities who exhibit motor impairments.
行走能力的出现是幼儿期关键的运动里程碑，能增强与环境的互动。患有神经发育障碍的幼儿往往存在非典型运动发育，这会影响其后期发展结果。使用姿态估计的基于视频的步态分析为标准化运动评估提供了一种替代方法，标准化运动评估具有主观性且在某些人群中难以确定，然而在确定其在幼儿中的准确性方面所做的工作很少。为填补这一空白，本研究旨在评估姿态估计用于不同发育水平儿童步态分析的可行性和准确性。
我们使用ProtoKinetics Zeno Walkway系统分析了112名有或无发育障碍的幼儿（平均年龄：30个月，标准差：8个月）的地面行走步态。同时录制的视频在OpenPose中进行处理以进行姿态估计，并通过自定义的MATLAB工作流程计算平均时空步态参数。使用Pearson相关性来比较OpenPose和Zeno Walkway在速度、步长和步时方面的差异。采用Bland-Altman分析（差异与平均值）来评估两种方法之间的一致性并确定均值差异。使用Mullen早期学习量表评估发育水平。
我们的分析纳入了患有自闭症（n = 77）、非自闭症发育问题（n = 6）、结节性硬化症（n = 13）、22q缺失（n = 1）和发育正常（n = 15）的儿童。Mullen早期学习综合得分范围为49至95（平均值 = 80.91，标准差 = 26.68）。OpenPose和Zeno Walkway在速度（r = 0.87，p < 0.0001）、步长（r = 0.79，p < 0.0001）和步时（r = 0.96，p < 0.0001）方面均高度相关，均值的绝对差异分别为0.04 m/s、0.03 m和0.０1 s。
我们的结果表明，使用姿态估计的基于视频的步态分析在不同发育水平的幼儿中是准确的。基于视频的步态分析成本低，可用于在自然环境（如参与者家中）进行远程数据收集。这些优势为使用重复测量来增加我们对儿科人群步态能力随时间变化的了解以及改进临床筛查工具提供了可能性，特别是对于那些表现出运动障碍的神经发育障碍患者。
