假肢跌倒预警中基于相关性分析的模糊自适应反馈调节

doi:10.13973/j.cnki.robot.2015.0732

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引用本文:
陈国兴, 耿艳利, 刘作军, 杨鹏. 假肢跌倒预警中基于相关性分析的模糊自适应反馈调节[J]. 机器人, 2015, 37(6): 732-737,747.DOI: 10.13973/j.cnki.robot.2015.0732. CHEN Guoxing, GENG Yanli, LIU Zuojun, YANG Peng. Fuzzy Adaptive Feedback Regulation for Stumble Pre-warning of Lower Limb ProsthesisBased on the Correlation Analysis. ROBOT, 2015, 37(6): 732-737,747. DOI: 10.13973/j.cnki.robot.2015.0732.

摘要

针对穿戴假肢的初期患者跌倒可能性较高的问题，提出一种基于相关性分析的模糊自适应反馈调节跌倒预警方法．应用支持向量机回归方法改进不同受试者的相关性分析模板，并对2种传感器的预测值进行融合以进行跌倒判别．由于不同患者应对跌倒时身体反应不同，采用具有反馈调节单元的模糊自适应变权重组合预测算法对不同患者的融合权重进行参数寻优，以增强下肢假肢跌倒预警系统的灵活性，最后采集多名受试者的数据进行分析论证该方法的可行性．实验结果表明，其预警正确率可达95%．该系统可实际应用于初期患者的康复训练，以提高假肢的安全性．

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关键词 ：模糊自适应, 跌倒预警, 相关性分析, 支持向量机, 反馈调节

Abstract：

Prosthesis users are easier to fall down at the early stage. In order to solve this problem, a stumble pre-warning method based on the correlation analysis with fuzzy adaptive feedback regulation is proposed. Support vector machine(SVM) regression method is applied to improving correlation analysis template of different subjects, and the predictive value of two sensors are fused to distinguish stumble. For different patients, the body reaction is different in the case of stumble. A combined forecast algorithm based on fuzzy adaptive variable weight with feedback regulation unit is applied to parameter optimization for the weights of different patients, and the flexibility of stumble pre-warning system for lower limb prosthesis is improved. Finally, the feasibility of the proposed method is demonstrated through analyzing the data collected from several subjects. The experimental results show that the pre-warning correct rate is 95%. The system can be applied to rehabilitation training of patients in the early stage, so as to improve the safety of prosthesis.

Key words： fuzzy adaptive stumble pre-warning correlation analysis support vector machine feedback regulation

收稿日期: 2015-07-01 录用日期: 2015-09-22

TP277

基金资助:

国家自然科学基金资助项目（61174009，61203323）

通讯作者: 耿艳利，c702hebut@163.com E-mail: c702hebut@163.com

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2015.0732 或 https://robot.sia.cn/CN/Y2015/V37/I6/732

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