Gillette Children's Specialty Healthcare, St. Paul, MN 55101, United States.
Gait Posture. 2011 Apr;33(4):730-2. doi: 10.1016/j.gaitpost.2011.02.014. Epub 2011 Mar 31.
This article introduces a new index, the GDI-Kinetic; a direct analog of the GDI based on joint kinetics rather than kinematics. The method consists of: (1) identifying "features" of the raw gait kinetic data using singular value decomposition, (2) identifying a subset of features that account for a large percentage of the information in the raw gait kinetic data, (3) expressing the raw data from a group of typically developing children as a linear combination of these features, (4) expressing a subject's raw data as a linear combination of these features, (5) calculating the magnitude of the difference between the subject and the mean of the control, and (6) scaling and transforming the difference, in order to provide a simple, and statistically well-behaved, measure. Linear combinations of the first 20 gait features produced a 91% faithful reconstruction of the data. Concurrent and face validity for the GDI-Kinetic are presented through comparisons with the GDI, Gillette Functional Assessment Questionnaire Walking Scale (FAQ), and topographic classifications within the diagnosis of Cerebral Palsy (CP). The GDI-Kinetic and GDI are linearly related but not strongly correlated (r(2)=0.24). Like the GDI, the GDI-Kinetic scales with FAQ level, distinguishes levels from one another, and is normally distributed across FAQ levels six to ten, and among typically developing children. The GDI-Kinetic also scales with respect to clinical involvement based on topographic CP classification in Hemiplegia types I-IV, Diplegia, Triplegia, and Quadriplegia. The GDI-Kinetic complements the GDI in order to give a more comprehensive measure of gait pathology.
本文介绍了一种新的指数,即 GDI-Kinetic;这是一种基于关节动力学而不是运动学的 GDI 的直接模拟。该方法包括:(1)使用奇异值分解识别原始步态动力学数据中的“特征”;(2)识别出能解释原始步态动力学数据中大部分信息的特征子集;(3)用这些特征来表示一组正常发育儿童的原始数据;(4)用这些特征来表示个体的原始数据;(5)计算个体与对照组平均值之间的差异大小;(6)对差异进行缩放和转换,以便提供一个简单且统计上表现良好的测量方法。前 20 个步态特征的线性组合可以忠实地重建数据的 91%。通过与 GDI、Gillette 功能评估问卷行走量表(FAQ)以及脑瘫(CP)诊断中的地形分类进行比较,提出了 GDI-Kinetic 的同时和表面有效性。GDI-Kinetic 与 GDI 呈线性相关,但相关性不强(r(2)=0.24)。与 GDI 一样,GDI-Kinetic 与 FAQ 水平相关,能够区分不同水平,并且在 FAQ 水平 6 到 10 之间以及正常发育儿童中呈正态分布。GDI-Kinetic 还可以根据 I-IV 型偏瘫、双瘫、截瘫和四肢瘫的地形 CP 分类以及临床参与程度进行缩放。GDI-Kinetic 补充了 GDI,以提供更全面的步态病理测量方法。