A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data
A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data
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DOI：
10.1016/j.jbiomech.2004.05.002
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发表时间：
2005-03-01
影响因子：
2.4
通讯作者：
Kamruzzaman, J
中科院分区：
文献类型：
--
作者：
Begg, R;Kamruzzaman, J
本文研究了应用机器学习方法（支持向量机，SVM）的步态变化，由于老化的自动识别使用三种类型的步态措施：基本的时间/空间，动力学和运动学。12名年轻人和12名老年人的步态进行了记录和分析，使用同步PEAK运动分析系统和力平台在正常行走。共提取了24个步态特征描述了这三种类型的步态特征，用于步态识别模型的开发和泛化性能的测试。测试结果表明，91.7%的支持向量机在其能力，以区分两种步态模式的整体准确性。SVM的分类能力被发现是不受影响的六个核函数（线性，多项式，径向基，指数径向基，多层感知器和样条）。从不同的步态数据类型中选取特征，可以提高步态识别率。特征选择算法表明，少至三个步态特征，从每个数据类型中选择一个，可以有效地区分年龄组，准确率为100%。这些结果表明，相当大的潜力，应用支持向量机在步态分类的许多应用。(C)2004爱思唯尔有限公司保留所有权利。
This paper investigated application of a machine learning approach (Support vector machine, SVM) for the automatic recognition of gait changes due to ageing using three types of gait measures: basic temporal/spatial, kinetic and kinematic. The gaits of 12 young and 12 elderly participants were recorded and analysed using a synchronized PEAK motion analysis system and a force platform during normal walking. Altogether, 24 gait features describing the three types of gait characteristics were extracted for developing gait recognition models and later testing of generalization performance. Test results indicated an overall accuracy of 91.7% by the SVM in its capacity to distinguish the two gait patterns. The classification ability of the SVM was found to be unaffected across six kernel functions (linear, polynomial, radial basis, exponential radial basis, multi-layer perceptron and spline). Gait recognition rate improved when features were selected from different gait data type. A feature selection algorithm demonstrated that as little as three gait features, one selected from each data type, could effectively distinguish the age groups with 100% accuracy. These results demonstrate considerable potential in applying SVMs in gait classification for many applications. (C) 2004 Elsevier Ltd. All rights reserved.