Flat foot functional evaluation using pattern recognition of ground reaction data
Flat foot functional evaluation using pattern recognition of ground reaction data
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DOI：
10.1016/s0268-0033(98)90099-7
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发表时间：
1999-08-01
影响因子：
1.8
通讯作者：
Catani, F
中科院分区：
文献类型：
--
作者：
Bertani, A;Cappello, A;Catani, F
Objective.本研究的主要目的是应用定量步态分析和统计模式识别作为临床决策辅助扁平足诊断和术后监测。设计。应用统计模式识别技术通过测量地面反作用力来区分正常和扁平足人群;将地面反作用力时间过程假设为足部功能的敏感指标。步态分析在骨科、术前规划、术后监测和不同治疗技术的后验评估中被认为是一种重要的临床工具。统计模式识别技术已在该领域取得成功，用于识别不同病理中选定运动功能的最重要变量，并设计分类规则和定量评估评分。方法。在自由速度赤脚行走过程中，记录了28名健康受试者的地面反作用力，并选择了28名有症状的灵活扁平足儿童进行手术干预。提出了一种新的基于启发式优化的特征选择算法，用于选择最具鉴别力的地面反作用力时间样本。一个两阶段的模式识别系统，由三个线性特征提取器，一个为每个地面反作用力分量，和一个线性分类器组成，被设计成使用所选的功能来分类每个主题的脚。分类器的输出用于定义功能评分。分类器将每个受试者执行的地面反作用力模式分配到正确的类中，估计误差为15%，对应于每个受试者的脚的分配误差为9%。选择的最具判别力的地面反作用力时间样本与有症状的柔性扁平足的病理生理学完全一致。获得的评分用于监测两个不同治疗亚组的32名柔性扁平足受试者术后1年和2年的功能恢复。统计模式识别技术是有前途的工具，临床步态分析，所获得的分数提供了重要的功能信息，可用于进一步帮助扁平足和不同的手术治疗技术的临床评价。
Objective. Main purpose of this study was to apply quantitative gait analysis and statistical pattern recognition as clinical decision-making aids in flat foot diagnosis and post-surgery monitoring.Design. Statistical pattern recognition techniques were applied to discriminate between normal and flat foot populations through ground reaction force measurements; ground reaction forces time course was assumed as a sensible index of the foot function.Background. Gait analysis is becoming recognized as an important clinical tool in orthopaedics, in pre-surgery planning, postsurgery monitoring and in a posteriori evaluation of different treatment techniques. Statistical pattern recognition techniques have been utilized with success in this field to identify the most significant variables of selected motor functions in different pathologies, and to design classification rules and quantitative evaluation scores.Methods. Ground reaction forces were recorded during free speed barefoot walks on 28 healthy subjects, and 28 symptomatic flexible flat foot children selected for surgical intervention. A new feature selection algorithm, based on heuristic optimization, was applied to select the most discriminant ground reaction forces time samples. A two-stage pattern recognition system, composed by three linear feature extractors, one for each ground reaction force component, and a linear classifier, was designed to classify the feet of each subject using the selected features. The output of the classifier was used to define a functional score.Results. The classifier assigned the ground reaction force patterns performed by each subject into the right class with an estimated error of 15%, corresponding to an assignment error for each subject's foot of 9%. The most discriminant ground reaction forces time samples selected are in full agreement with the pathophysiology of the symptomatic flexible flat foot. The obtained score was utilized to monitor the 1 and 2 years post-operative functional recovery of two differently treated subgroups of 32 flexible flat foot subjects.Conclusions. Statistical pattern recognition techniques are promising tools for clinical gait analysis; the obtained score provides important functional information that could be used as a further aid in the clinical evaluation of flat foot and different surgical treatment techniques.