前交叉韧带断裂后足底压力特征的聚类分析

运动过程中，人体的步态特征可以在足底压力图像上有准确的记录，而这也就可以成为判断步态正常与否的一条有效依据.通过一组压力传感器阵列获取人体运动过程的足底压力分布数据，提取步态的运动学和动力学特性.在此基础上，采用极限学习机（Extreme learning machines，ELM）神经网络聚类算法对足底压力数据进行分析，完成正常与异常步态的分类辨识工作.本文从实际临床数据出发，对前交叉韧带断裂患者进行步态分析，并据医生的临床诊断结果进行校验.该方法在步态分析上取得了较为良好的效果，仿真结果表明了其有效性.

Cluster Analysis of Plantar Pressure Characteristics after Anterior Cruciate Ligament Deficiency

The gait characteristics of an actor can be recorded accurately on the plantar pressure map in a movement. It can be used to distinguish whether the gait of this actor in a movement is abnormal or not. Using a set of pressure sensors, the plantar pressure during dynamic motion is collected, and the kinetic and dynamic characteristics of gait are extracted. Then extreme learning machines (ELM) neural network cluster algorithm is used to the analyze of the plantar pressure data and identification of normal or abnormal gait is done. Based on actual clinical data, this method carries out an analysis of patients with anterior cruciate ligament deficiency, which is checked according to the doctor's clinical diagnosis results. Result shows that this method is effective.