基于时—频分析的步态模式自动分类  被引量:9

Automated classification of gait patterns based on time-frequency analysis

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作  者:王斐[1,2] 闻时光[1] 张育中[1] 金基准[1] 吴成东[1] 

机构地区:[1]东北大学流程工业综合自动化国家重点实验室,沈阳110819 [2]哈尔滨工业大学机器人技术与系统国家重点实验室,哈尔滨150001

出  处:《北京科技大学学报》2012年第1期31-36,共6页Journal of University of Science and Technology Beijing

基  金:中央高校基础科研基金资助项目(90404007);机器人技术与系统国家重点实验室开放课题基金资助项目(SKLRS--2010--ZD--03)

摘  要:针对不同路况和运动模式下的高维、非线性、强耦合和高时变下肢加速度信号的识别问题,提出了一种基于时--频分析的步态模式自动分类方案.利用三轴加速度传感器采集运动时小腿在矢状面、冠状面和横切面的加速度信号,利用五阶Daubechies小波基对其进行特征提取,并采用线性判别式分析进行降维,最后利用决策树和支持向量机对得到的精简步态特征进行模式分类.实验结果显示两种分类器的总体分类准确率均达到90%以上,个别步态分类可达到100%,验证了特征提取和降维方法的合理性和有效性.A general scheme for the automated classification of gait patterns based on time-frequency analysis was proposed to discriminate acceleration signals characterized by high dimension, non-linearity, strong coupling and high time-varying acquired under different terrains and motion patterns of lower limbs. A three-axis acceleration sensor was mounted on a crns to acquire acceleration signals in the sagittal, coronal and cross-sectional planes separately. By using a 5-order Daubechies wavelet base, the features were extracted from time-series acceleration signals and further dimensionally reduced by employing linear discrimination analysis (LDA). The reduced features were classified by the decision tree and the support vector machine ( SVM). From experimental results, both classifiers can achieve the high classification accuracy ratio over 90% and for the specified gait the ratio can be up to 100% , indicating the rationality and effectiveness of the proposed methods for feature extraction and dimension reduction.

关 键 词:步态分析 模式分类 加速度测量 小波分析 决策树 支持向量机 

分 类 号:TP183[自动化与计算机技术—控制理论与控制工程]

 

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