Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier
Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier
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DOI：
10.1109/tbme.2007.905388
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发表时间：
2008-03-01
影响因子：
4.6
通讯作者：
de Guise, Jacques A.
中科院分区：
文献类型：
--
作者：
Mezghani, Neila;Husse, Sabine;de Guise, Jacques A.
这项工作的目的是开发一种自动计算机方法，利用三维地面反作用力（GRF）测量来区分无症状（AS）和骨关节炎（OA）的膝关节步态模式。首先从力向量随时间的变化中提取GRF特征，然后根据最近邻规则进行分类。我们研究了两个不同的特征：多项式展开的系数和小波分解的系数。我们还分析了GRIT的每个组成部分（垂直、前后和内外侧）对分类的影响。小波分解的正前方和内侧外侧分量的识别率最高，达到91%。这些结果证明了该表示和分类器对AS和OA膝关节步态模式自动分类的有效性。他们还强调了前后侧力和内侧侧力在步态模式分类中的相关性。
The aim of this work is to develop an automatic computer method to distinguish between asymptomatic (AS) and osteoarthritis (OA) knee gait patterns using 3-D ground reaction force (GRF) measurements. GRF features are first extracted from the force vector variations as a function of time and then classified by the nearest neighbor rule. We investigated two different features: the coefficients of a polynomial expansion and the coefficients of a wavelet decomposition. We also analyzed the impact of each GRIT component (vertical, anteroposterior, and medial lateral) on classification. The best discrimination rate (91 %) was achieved with the wavelet decomposition using the anteroposterior and the medial lateral components. These results demonstrate the validity of the representation and the classifier for automatic classification of AS and OA knee gait patterns. They also highlight the relevance of the anteroposterior and medial lateral force components in gait pattern classification.