Department of Orthopedics, Orebro University School of Medical Sciences and Orebro University Hospital, Orebro, Sweden.
Department of Electronics and Telecommunications, Politecnico di Torino, Corso Castelfidardo, 39, Torino, 10129, Italy.
BMC Musculoskelet Disord. 2024 Sep 17;25(1):747. doi: 10.1186/s12891-024-07853-9.
Gait analysis aids in evaluation, classification, and follow-up of gait pattern over time in children with cerebral palsy (CP). The analysis of sagittal plane joint kinematics is of special interest to assess flexed knee gait and ankle joint deviations that commonly progress with age and indicate deterioration of gait. Although most children with CP are ambulatory, no objective quantification of gait is currently included in any of the known international follow-up programs. Is video-based 2-dimensional markerless (2D ML) gait analysis with automated processing a feasible and useful tool to quantify deviations, evaluate and classify gait, in children with CP?
Twenty children with bilateral CP with Gross Motor Function Classification Scale (GMFCS) levels I-III, from five regions in Sweden, were included from the national CP registry. A single RGB-Depth video camera, sensitive to depth and contrast, was positioned laterally to a green walkway and background, with four light sources. A previously validated markerless method was employed to estimate sagittal plane hip, knee, ankle kinematics, foot orientation and spatio-temporal parameters including gait speed and step length.
Mean age was 10.4 (range 6.8-16.1) years. Eight children were classified as GMFCS level I, eight as II and four as III. Setup of the measurement system took 15 min, acquisition 5-15 min and processing 50 min per child. Using the 2D ML method kinematic deviations from normal could be determined and used to implement the classification of gait pattern, proposed by Rodda et al. 2001.
2D ML assessment is feasible, since it is accessible, easy to perform and well tolerated by the children. The 2D ML adds consistency and quantifies objectively important gait variables. It is both relevant and reasonable to include 2D ML gait assessment in the evaluation of children with CP.
步态分析有助于评估、分类和随时间推移跟踪脑瘫(CP)儿童的步态模式。矢状面关节运动学分析特别有趣,可用于评估常见的膝关节弯曲步态和踝关节偏差,这些偏差会随着年龄的增长而进展,并表明步态恶化。尽管大多数 CP 儿童都能行走,但目前在任何已知的国际随访计划中都没有包括对步态的客观量化。基于视频的二维无标记(2D ML)步态分析和自动处理是否是一种可行且有用的工具,可用于量化偏差、评估和分类 CP 儿童的步态?
从瑞典五个地区的全国 CP 注册中心纳入了 20 名双侧 CP 且粗大运动功能分级系统(GMFCS)级别为 I-III 的儿童。一个对深度和对比度敏感的单台 RGB-Depth 摄像机被放置在绿色步道和背景的侧面,配有四个光源。使用先前经过验证的无标记方法来估计矢状面髋关节、膝关节、踝关节运动学、足的方向和时空参数,包括步态速度和步长。
平均年龄为 10.4 岁(范围 6.8-16.1 岁)。8 名儿童被分类为 GMFCS 级别 I,8 名儿童为 II 级,4 名儿童为 III 级。测量系统的设置需要 15 分钟,采集需要 5-15 分钟,每个孩子的处理时间为 50 分钟。使用 2D ML 方法可以确定与正常情况的运动学偏差,并用于实施由 Rodda 等人提出的步态模式分类。2001.
2D ML 评估是可行的,因为它易于获得、易于执行且儿童易于耐受。2D ML 增加了一致性并客观量化了重要的步态变量。在 CP 儿童的评估中包括 2D ML 步态评估既相关又合理。