Muniz A M S, Manfio E F, Andrade M C, Nadal J
Biomed. Eng. Program, Fed. Univ. of Rio de Janeiro.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:2683-6. doi: 10.1109/IEMBS.2006.259820.
This study aims at testing the application of principal component analysis (PCA) in the ground reaction force (GRF) in discriminating the gait pattern between normal and abnormal subjects, and assessing the rehabilitation treatment. The sample was composed by 31 subjects, organized into two groups: a control group (CG) of 25 normal and a group (FG) of six patients with lower limb fractures, which was considered before (FGB) and after (FGA) a treadmill physiotherapeutic treatment. The vertical component of GRF data was collected with an instrumentized treadmill. PCA method was applied and the first two coefficients (PCC) were obtained for the three groups. The region of CG values was separated in the PCC plane with the elliptical area of displacement and with a linear threshold between CG and FGB obtained by stepwise logistic regression. Results show that all values of FGA moved towards CG region from the corresponding FGB position, indicating the potential power of PCA in discriminating between normal and abnormal gait and objectively evaluating the effects of rehabilitation treatment.
本研究旨在测试主成分分析(PCA)在地面反作用力(GRF)中用于区分正常和异常受试者步态模式以及评估康复治疗的应用。样本由31名受试者组成,分为两组:25名正常受试者的对照组(CG)和6名下肢骨折患者组(FG),该组在跑步机物理治疗前(FGB)和治疗后(FGA)进行了评估。使用仪器化跑步机收集GRF数据的垂直分量。应用PCA方法并获得三组的前两个系数(PCC)。通过逐步逻辑回归在PCC平面中用位移椭圆区域和CG与FGB之间的线性阈值将CG值区域分开。结果表明,FGA的所有值从相应的FGB位置向CG区域移动,表明PCA在区分正常和异常步态以及客观评估康复治疗效果方面的潜在能力。