• CN:11-2187/TH
  • ISSN:0577-6686

›› 2013, Vol. 49 ›› Issue (13): 17-23.

• 论文 • 上一篇    下一篇

基于SVM-GDFNN的上肢康复训练机器人处方诊断

潘礼正;宋爱国;徐国政;李会军;徐宝国   

  1. 东南大学仪器科学与工程学院;南京邮电大学自动化学院
  • 发布日期:2013-07-05

Prescription Diagnosis for Upper-limb Rehabilitation Training Robot Based on SVM-GDFNN

PAN Lizheng;SONG Aiguo;XU Guozheng;LI Huijun;XU Baoguo   

  1. School of Instrument Science and Engineering, Southeast University College of Automation, Nanjing University of Posts and Telecommunications
  • Published:2013-07-05

摘要: 针对目前上肢康复训练机器人缺少对患肢训练模式智能处方诊断的问题,提出基于支持向量机和广义动态模糊神经网络(Support vector machine and generalized dynamic fuzzy neural networks, SVM-GDFNN)相融合的处方诊断方法。利用SVM采用结构风险最小原则,具有很好泛化能力的特点,对样本进行初步处方诊断。同时针对在SVM支持矢量附近区域样本易于出现错诊现象,利用GDFNN网络对支持矢量附近区域样本进行复诊,最终根据设计的诊断原则对患肢运动训练模式进行确诊。结合临床试验运用,分析患肢运动性能特征提取方法,阐述SVM-GDFNN处方诊断方法的模型建立以及测试诊断过程。临床试验结果表明该方法能够有效地减少样本的错诊现象,具有较高的诊断准确率;实现运动训练模式智能处方诊断功能,有助于提高康复训练机器人临床智能化水平。

关键词: 广义动态模糊神经网络, 康复机器人, 支持向量机, 智能诊断

Abstract: Considering the lacking function of existing upper-limb rehabilitation training robot to automatically recommend a suitable training mode for the impaired limb, a prescription diagnosis method combining the support vector machine and generalized dynamic fuzzy neural networks (SVM-GDFNN) is proposed. Based on the principle to minimize structure risk, SVM does very well in generalization ability, which is used to recommend a preliminary prescription diagnosis for the sample. At the same time for the SVM method is prone to make wrong diagnosis in the support vector area, GDFNN is employed to conduct referral for the samples in the area, and then the training mode of impaired limb is prescribed according to the designed principles. Combining the clinical application, the feature extraction method for impaired-limb movement performance is analyzed, and the process of the SVM-GDFNN model building and sample diagnosis are presented in detail. Clinical experiment results indicate that the suggested method can effectively reduce the fault diagnosis and serve with a high diagnostic accuracy rate. By designing function to automatically recommend training mode, it is helpful to improve the clinical intelligent level of rehabilitation training robots.

Key words: Generalized dynamic fuzzy neural networks, Intelligent diagnosis, Rehabilitation robot, Support vector machine

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