Clinical gait analysis and the interpretation of related records are a powerful tool to aid clinicians in the diagnosis, treatment and prognosis of human gait disabilities. The aim of this study is to investigate kinematic, kinetic, and electromyographic (EMG) data from child patients with spastic hemiplegia (SH) in order to discover useful patterns in human gait. Data mining techniques and classification algorithms were used to explore data from 278 SH patients. We studied different techniques for selection of attributes in order to get the best classification scores. For kinematics data, the dimension of the initial attribute space was 1033, which was reduced to 78 using the Ranker and FilteredAttributeEval algorithms. For kinetics data, the best combination of attributes was determined by SubsetSizeForward Selection and CfsSubEval with a reduction of attribute space size from 931 to 25. Decision-tree based learning algorithms, in particular the logistic model tree based on logistic regression and J48, produced the best scores for correct SH gait classification (89.393% for kinetics, 89.394% for kinematics, and 97.183% for EMG). To evaluate the effectiveness of combined feature selection methods with the classifiers, quantitative measures of model quality were used (kappa statistic, measures of sensitivity and specificity, verisimilitude rates, and ROC curves). Comparison of these results to a qualitative assessment from physicians showed a success rate of 100% for results from kinematics and EMG data, while for kinetics data the success rate was 60%. The patterns resulting from automatic data analysis of gait records have been integrated into an end-user application in order to support medical decision-making.

临床步态分析和相关记录的解释是帮助临床医生诊断，治疗和预测人类步态障碍的有力工具。这项研究的目的是调查患有痉挛性偏瘫（SH）的儿童患者的运动学，动力学和肌电（EMG）数据，以发现人类步态的有用模式。数据挖掘技术和分类算法用于研究278例SH患者的数据。我们研究了用于选择属性的不同技术，以获得最佳分类分数。对于运动学数据，初始属性空间的维数为1033，使用Ranker和FilteredAttributeEval算法将其减小为78。对于动力学数据，属性的最佳组合由SubsetSizeForward Selection和CfsSubEval确定，属性空间大小从931减少到25。基于决策树的学习算法，特别是基于logistic回归和J48的logistic模型树，产生了正确的最佳分数。 SH步态分类（动力学的89.393％，运动学的89.394％和EMG的97.183％）。为了评估结合分类器的特征选择方法的有效性，使用了模型质量的定量度量（kappa统计量，敏感性和特异性的度量，真实率和ROC曲线）。将这些结果与医生进行的定性评估相比较，从运动学和EMG数据得出的结果成功率为100％，而从动力学数据得出的成功率为60％。