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使用人体下部行走数据进行模式分析,以识别步态印记.

Tyler M Wiles1, Seung Kyeom Kim1, Nick Stergiou1,2

  • 1Department of Biomechanics at the University of Nebraska at Omaha, 6160 University Dr S, Omaha, NE 68182, USA.

Computational and structural biotechnology journal
|April 22, 2024
PubMed
概括

人们拥有一种独特而持久的步行模式,称为步行印记,它允许个人识别. 先进的模式分析技术在识别个人基于他们的步行印记方面取得了很高的准确性.

关键词:
生物识别信息 生物识别信息步态识别 步态识别 步态识别惯性测量单位 惯性测量单位随机的森林 随机的森林支持矢量机器 支持矢量机器可变性的变化.

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科学领域:

  • 生物识别信息 生物识别信息
  • 人类运动分析分析.
  • 模式识别 模式识别 模式识别

背景情况:

  • 人类的指纹是独特的,终身的标识符.
  • 一个类似的独特和持久的标识符可能存在于一个人的步行模式中,称为步行纹.

研究的目的:

  • 为了证明存在一个独特的步态印记.
  • 用基本的时空步态变量来识别个人.

主要方法:

  • 81名成年人穿着惯性测量单元在地面上行走.
  • 数据收集在多个试验在不同的日子.
  • 四种模式分析方法 (欧几里德距离,等号相似性,随机森林,支向量机) 应用于时空变量.

主要成果:

  • 在基于步态模式的个人识别中具有高准确性.
  • 随机森林实现了98.63%的准确性.
  • 支持向量机和共弦相似性 (前10个试验) 实现了98.40%的准确性.

结论:

  • 一个持久的行走模式包含足够的个人信息来识别.
  • 这些发现支持存在一种独特的步履痕迹,类似于指纹.