基于时-频分析的步态模式自动分类

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.

Automated classification of gait patterns based on time-frequency analysis

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 crus 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.