Lai Daniel T H, Levinger Pazit, Begg Rezaul K, Gilleard Wendy Lynne, Palaniswami Marimuthu
Biomechanics Unit, Centre for Ageing, Rehabilitation, Exercise, and Sport, Victoria University, Melbourne, Vic. 8001, Australia.
IEEE Trans Inf Technol Biomed. 2009 Sep;13(5):810-7. doi: 10.1109/TITB.2009.2022927. Epub 2009 May 12.
Patellofemoral pain syndrome (PFPS) is a common disorder that afflicts people across all age groups, and results in various degrees of knee pain. The diagnosis of PFPS is difficult since the exact biomechanical factors and the extent to which they are affected by the disorder are still unknown. Recent research has reported significant statistical differences in ground reaction forces (GRFs) and foot kinematics, which could be indicative of PFPS, but the interrelationship between many of these measures and the pathology have been absent so far. In this paper, we applied the support vector machines (SVMs) to detect PFPS gait based on 14 GRF and 16 foot kinematic features recorded from 27 subjects (14 healthy and 13 with PFPS). The influence of combined gait parameters on classification performance was investigated through the use of a feature-selection algorithm. The optimal feature set was then compared against the most statistically significant individual features (p < 0.05) found by previous study. Test results indicated that GRF features alone resulted in a higher leave-one-out (LOO) classification accuracy (85.15%) compared to 74.07% using only kinematic features. A hill-climbing feature-selection algorithm was applied to determine the subset of combined kinematic and kinetic features, which provided optimal classifier performance. This subset, which consists of six features (two from GRF and four from foot kinematic features), provided an improved LOO accuracy of 88.89% . The optimal feature set detected by the SVM, which best identified gait characteristics of PFPS, was found to be closely related to inferential statistical analysis with the added distinction that the SVM could potentially be deployed as an automated system for detecting gait changes in patients with PFPS.
髌股疼痛综合征（PFPS）是一种常见疾病，困扰着所有年龄段的人群，并导致不同程度的膝关节疼痛。PFPS的诊断很困难，因为确切的生物力学因素及其受该疾病影响的程度仍不清楚。最近的研究报告了地面反作用力（GRFs）和足部运动学方面存在显著的统计学差异，这可能表明患有PFPS，但迄今为止，这些测量指标中的许多与病理学之间的相互关系尚不明确。在本文中，我们应用支持向量机（SVMs），基于从27名受试者（14名健康者和13名患有PFPS者）记录的14个GRF和16个足部运动学特征来检测PFPS步态。通过使用特征选择算法研究了组合步态参数对分类性能的影响。然后将最佳特征集与先前研究中发现的最具统计学意义的个体特征（p < 0.05）进行比较。测试结果表明，仅GRF特征的留一法（LOO）分类准确率更高（85.15%），而仅使用运动学特征时为74.07%。应用爬山特征选择算法来确定运动学和动力学组合特征的子集，该子集提供了最佳的分类器性能。这个由六个特征组成的子集（两个来自GRF，四个来自足部运动学特征），留一法准确率提高到了88.89%。发现支持向量机检测到的最佳识别PFPS步态特征的最佳特征集与推断性统计分析密切相关，此外，支持向量机有可能被部署为一个自动系统，用于检测PFPS患者的步态变化。
