Wu Jianning, Wu Bin
School of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350007, China.
Hospital of Fujian Normal University, Fuzhou 350007, China.
Biomed Res Int. 2015;2015:528971. doi: 10.1155/2015/528971. Epub 2015 Feb 2.
The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.
在临床应用中，准确识别步态不对称对于评估高危步态非常有益。本文基于这样的假设，即步态动力系统的内在变化程度与下肢左右两侧步态变量之间的不同统计分布相关，研究了基于统计学习算法的分类方法在量化步态对称性方面的应用；也就是说，下肢之间相似性微小差异的辨别被视为它们不同概率分布的重新组织。在正常行走过程中，使用应变片测力平台记录了60名参与者的动态步态数据。该分类方法基于支持向量机算法等先进的统计学习算法设计用于二分类，并被用于定量评估步态对称性。实验结果表明，所提出的方法能够捕捉步态变量中隐藏的更多内在动态信息，并以卓越的泛化性能识别左右步态模式。此外，与传统的步态对称性指数方法相比，我们提出的技术能够识别下肢之间微小的显著差异。所提出的算法将成为临床诊断中早期识别老年人步态不对称的有效工具。
