School of Kinesiology and Health Studies, Queen's University, Kingston, ON, Canada.
Gait Posture. 2010 Feb;31(2):197-203. doi: 10.1016/j.gaitpost.2009.10.005. Epub 2009 Nov 17.
Principal component analysis (PCA) has been used to reduce the volume of gait data and can also be used to identify the differences between populations. This approach has not been used on stair climbing gait data. Our objective was to use PCA to compare the gait patterns between young and older adults during stair climbing.
The knee joint mechanics of 30 healthy young adults (23.9 + or - 2.6 years) and 32 healthy older adults (65.5 + or - 5.2 years) were analyzed while they ascended a custom 4-step staircase. The three-dimensional net knee joint forces, moments, and angles were calculated using typical inverse dynamics. PCA models were created for the knee joint forces, moments and angles about the three axes. The principal component scores (PC scores) generated from the model were analyzed for group differences using independent samples t-tests. A stepwise discriminant procedure determined which principal components (PCs) were most successful in differentiating the two groups.
The number of PCs retained for analysis was chosen using a 90% trace criterion. Of the scores generated from the PCA models nine were statistically different (p < .0019) between the two groups, four of the nine PC scores could be used to correctly classify 95% of the original group.
The PCA and discriminant function analysis applied in this investigation identified gait pattern differences between young and older adults. Identification of stair gait pattern differences between young and older adults could help in understanding age-related changes associated with the performance of the locomotor task of stair climbing.
主成分分析（PCA）已被用于减少步态数据的量，并且还可以用于识别人群之间的差异。这种方法尚未用于楼梯攀爬步态数据。我们的目的是使用 PCA 来比较年轻人和老年人在楼梯攀爬过程中的步态模式。
分析了 30 名健康年轻人（23.9 ± 2.6 岁）和 32 名健康老年人（65.5 ± 5.2 岁）在爬上定制的 4 级楼梯时的膝关节力学。使用典型的逆动力学计算三维净膝关节力、力矩和角度。为膝关节力、力矩和三个轴上的角度创建了 PCA 模型。使用独立样本 t 检验分析从模型生成的主成分得分（PC 得分）的组间差异。逐步判别程序确定哪些主成分（PC）最成功地区分两组。
使用 90%轨迹标准选择要分析的 PC 数量。从 PCA 模型生成的得分中，有 9 个在两组之间存在统计学差异（p <.0019），9 个 PC 得分中有 4 个可以用于正确分类 95%的原始组。
本研究中应用的 PCA 和判别函数分析确定了年轻人和老年人之间的步态模式差异。识别年轻人和老年人之间的楼梯步态模式差异可能有助于理解与楼梯攀爬运动任务表现相关的与年龄相关的变化。
