摘要:步态数据分析是模式识别、数据挖掘与智能数据分析领域中的一个重要研究方向.对步态数据进行分析并求解步态周期段，是该领域的一个核心课题，其中，以波峰波谷识别、步态模板匹配、利用信号处理基本方法为主.这些方法尽管已取得了一定条件下的应用，但大多需要预知步态数目、步态模板数据等基本信息，其可用性受限.针对这一问题，提出一种结合了波峰波谷检测与阈值空间的高可用性步态周期分析方法，通过自动求解预估值并构建自适应区间，根据通用步态模型对缺乏上述信息的未知步态数据进行切分与分析，能够更便利准确地求解步态周期数据.同时也提出了一种过滤不相关数据的方法，用于过滤测试数据头尾的部分不相关数据.该方法与朴素快速傅里叶方法和本领域3类最新典型方法进行了对比，实验结果显示：在所有样本中，该方法的步态周期段求解准确度最高，使步态数据的分析与处理更贴合实际情况.



Abstract:Gait analysis is an important research in the fields of pattern recognition, data mining, and intelligent data analysis. Many different methods, such as recognition of crests and troughs, matching of gait templates, signal-processing based approaches, etc., have been proposed and applied, but these methods require presetting parameters such as gait numbers, gait templates, etc. Their feasibilities are limited. In this study, a new high feasible method, which can automatically calculate a presetting cycle value and a self-adaption range, is proposed, based on peak detection and threshold, to analyze gait cycle segment without unnecessary information needed by other methods. Moreover, a method to filter nonrelated data is proposed. Comparing with simple FFT and other three kinds of representative methods, the proposed approach has the best analysis results in evaluations.



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