中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (9): 1623-1626.doi: 10.3969/j.issn.1673-8225.2011.09.025

• 数字化骨科 digital orthopedics • 上一篇    下一篇

模糊聚类支持向量机在步态分类中的应用

陆  强，冯  敏，马  华，张西学   

  1. 泰山医学院信息工程学院，山东省泰安市  271016
  • 收稿日期:2010-10-25 修回日期:2011-01-22 出版日期:2011-02-26 发布日期:2011-02-26
  • 作者简介:陆强★，男，1975年生，山东省泰安市人，汉族，2007年山东科技大学毕业，硕士，讲师，主要从事医学信号处理和智能控制与仪表的研究。 luqiang271016@163.com

Application of support vector machines and fuzzy clustering algorithm in gait classification

Lu Qiang, Feng Min, Ma Hua, Zhang Xi-xue   

  1. College of Information & Engineering, Taishan Medical University, Taian  271016, Shandong Province, China
  • Received:2010-10-25 Revised:2011-01-22 Online:2011-02-26 Published:2011-02-26
  • About author:Lu Qiang★, Master, Lecturer, College of Information & Engineering, Taishan Medical University, Taian 271016, Shandong Province, China luqiang271016@163.com

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376

被引次数

2

摘要：

背景：对于患有神经系统或骨骼肌肉系统疾病的患者，分析步态数据可以评定康复程度，制定治疗方案。如何有效地分类小样本步态数据成为重要的研究课题。
目的：用改进的支持向量机算法对小样本步态数据进行分类，准确诊断疾病。
方法：建立加入模糊C均值聚类的支持向量机算法，选用Gait Dynamics in Neuro-Degenerative Disease Data Base 40~59岁年龄段的6组数据，共720个样本数据，采用左摆间隔和左支撑间隔两维参数对步态数据建模。数据归一化后，通过模糊C均值聚类对数据进行预处理；然后用支持向量机对数据进行分类。采用不同核函数的支持向量机算法验证分类能力。
结果与结论：实验结果表明，利用改进的支持向量机算法，可以有效地对信号进行分类，有助于疾病的诊断和治疗方案的制定。

关键词: 步态分类, 支持向量机, 模糊C均值聚类, 核函数, 智能诊断技术

Abstract:

BACKGROUND: The gait data is an objective parameter to patients who have musculoskeletal disorders or nervous system diseases. It can evaluate recovery of illness and set up treatment method. How to classify the gait data effectively has become an important research topic.
OBJECTIVE: In order to diagnosis illness effectively and provide scientific basis for setting up treatment method, using the modified support vector machines algorithm to classify gait data.
METHODS: Modified Support Vector Machines algorithm was proposed, and 720 samples were selected from 6 group data aged 40-59 years from Gait Dynamics in Neuro-degenerative Disease Data Base. Gait data models were established using left swing interval and left stance interval. After normalization, data were preprocessed with Fuzzy C-Mean algorithm, and then classified gait data utilizing support vector machines. The classification ability was verified by support vector machines algorithm with various kernel functions.
RESULTS AND CONCLUSION: By comparing classifiers using different kernel function, the result is that the classifier with modified support vector machines algorithm can classify small sample size gait data and set up treatment method effectively.

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