步态特征提取的K均值聚类自适应判别Adaptive gait features discrimination based on K means clustering
孙伟;宋如意;丁伟;
摘要(Abstract):
针对微惯性零速修正算法中步态特征的准确提取,以及步态特征的无规律性成为制约行人导航系统中步态信息提取与辨识的问题,该文提出一种基于K均值聚类自适应的行人步态特征辨别方法。分析行人步态规律并通过设定角速率阈值法对步态特征进行初判后,采用K均值聚类自适应算法设定时间阈值并将误判的步态进行纠正。为验证该算法的普适性,分别针对不同测试个体和同一个体5组不同行走速度条件下的步态特征判别实验,结果表明,本文提出的步态自适应判别方法对不同个体具有良好的适应性;为进一步验证K均值自适应步态判别算法对人员位置解算的准确性,分别开展圆形及400m跑道闭合行走实验,对比不同行走路径对应的位置误差可看出,解算位置误差虽然随行走距离增大而增加,但其相对误差均不超过2%。
关键词(KeyWords): 步态特征提取;K均值聚类;自适应;定位
基金项目(Foundation): 辽宁省教育厅高等学校基本科研项目(LJ2017FAL005);; 辽宁省“百千万人才工程”培养经费资助项目(辽百千万立项[2015]76号);; 城市空间信息工程北京市重点实验室经费资助项目(2018206)
作者(Authors): 孙伟;宋如意;丁伟;
DOI: 10.16251/j.cnki.1009-2307.2019.12.005
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