人体步态复杂度的递归图和递归定量分析研究

发布时间：2018-05-05 21:44

本文选题：递归图 + 递归定量分析　；参考：《西安交通大学学报》2017年10期

【摘要】：为了准确分辨并识别不同人体的步态特征,提出采用递归图和递归定量分析的方法,计算人体步态非线性时间序列的复杂度。首先利用互信息和伪邻近法分别计算得到合适的延时时间和嵌入维数,根据相空间重构的原理将一维时间序列扩展到高维相空间中,获得时间序列在高维空间中邻近点的分布规律和运动特点。构建了患有帕金森疾病的老年人、健康老年人和健康年轻人的步态信号递归图,可以直观定性分析和评估这3组人群的步态信号的空间分布程度,其中健康人群最复杂。采用递归定量分析,量化了人体步态的复杂度,结果表明,患有帕金森疾病的人群的步态复杂度最小,而且独立样本t检验显示了3组人群的复杂度具有显著的差异性。该方法简单可行,可以准确地对不同年龄和帕金森疾病的人群进行分类识别,有利于人体健康监测和诊断研究。
[Abstract]:In order to distinguish and identify the gait features of different human bodies, the method of recursion and recursive quantitative analysis is proposed to calculate the complexity of the human gait nonlinear time series. First, the appropriate time delay time and the embedding dimension are calculated by mutual information and pseudo proximity, and the one dimensional time sequence is based on the principle of phase space reconstruction. The sequence is extended to the high dimensional space, and the distribution and motion characteristics of the adjacent points in the high dimensional space are obtained. The gait signal recursion of the elderly, healthy and healthy young people with Parkinson disease can be analyzed and evaluated directly to analyze and evaluate the spatial distribution of the gait signals of the 3 groups of people. The complex degree of human gait was quantified by recursive quantitative analysis. The results showed that the gait complexity of the people with Parkinson's disease was the least, and the independent sample t test showed that the complexity of the 3 groups was significantly different. This method was simple and feasible, and it could be accurate to different ages and Parkinson's disease. Identifying and classifying sick people is beneficial to the research of human health monitoring and diagnosis.

【作者单位】：西安交通大学机械制造系统工程国家重点实验室;香港中文大学机械与自动化工程学系;
【基金】：国家自然科学基金资助项目(51575426,51611530547) 中央高校基本科研业务费综合交叉项目(xjj2016002)
【分类号】：R318

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