基于多源信息和广义回归神经网络的下肢运动模式识别

doi:10.13973/j.cnki.robot.2015.0310

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引用本文:
刘磊, 杨鹏, 刘作军. 基于多源信息和广义回归神经网络的下肢运动模式识别[J]. 机器人, 2015, 37(3): 310-317.DOI: 10.13973/j.cnki.robot.2015.0310. LIU Lei, YANG Peng, LIU Zuojun. Lower Limb Locomotion Modes Recognition Based on Multiple-source Information and General Regression Neural Network. ROBOT, 2015, 37(3): 310-317. DOI: 10.13973/j.cnki.robot.2015.0310.

摘要

针对目前下肢运动模式识别率低的问题，提出了一种基于多源信息和广义回归神经网络(GRNN)的下肢运动模式识别方法.通过足底压力信息将人体日常下肢动作分解为不同的动作片段以组成识别目标集.采用下肢表面肌电信号中的偏度、峭度、功率谱熵，以髋关节角度作为腿部特征值，利用主成分分析(PCA)方法对文中提取的特征值进行降维处理，以缩短模型训练时间，防止过拟合.最后，利用 GRNN 对目标集中平地行走、上楼、下楼 3 种动作进行识别.实验结果表明，该方法的正确识别率为 90.16%.

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关键词 ：多源信息, 片段分解, 主成分分析, 下肢运动模式识别, 广义回归神经网络

Abstract：

In order to improve recognition rate of lower limb locomotion modes, a method based on multiple-source information and general regression neural network (GRNN) is proposed. Users' daily lower limb locomotion modes are decomposed into different segments to form the recognition goals using the plantar pressure sensor. For surface electromyography (sEMG) signal, three features are used, i.e. skewness, kurtosis, and power spectral entropy. The hip joint angle is chosen as leg features. In order to decrease the time for training the models and to prevent overfitting, principal component analysis (PCA) is used to reduce the dimension of the extracted features. GRNN is used to recognize 3 kinds of motions, namely stairs ascent, stairs descent and level-ground walking. The experimental results show that the recognition correct rate is 90.16%.

Key words： multiple-source information fragment decomposition principal component analysis (PCA) lower limb locomotion mode recognition general regression neural network (GRNN)

收稿日期: 2014-07-02 录用日期: 2015-01-28

TP391

基金资助:

国家自然科学基金资助项目(61203323,61174009);天津市自然科学基金资助项目(13JCQNJC03400)

通讯作者: 杨鹏,yphebut@yahoo.com E-mail: yphebut@yahoo.com

作者简介: 刘磊(1984--),男,博士生.研究领域:模式识别,康复辅具.
杨鹏(1960--),男,博士,教授.研究领域:康复辅具,智能机器人,模式识别.
刘作军(1971--),男,博士,教授.研究领域:康复辅具,智能机器人,模式识别.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2015.0310 或 https://robot.sia.cn/CN/Y2015/V37/I3/310

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