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用无监督等级聚类来识别脚步的共变量分析.
概括
脚步识别生物识别系统受到年龄和体重等共变量的影响. 层次聚类揭示了不同的步态模式,表明这些因素可以改善或偏见识别系统.
科学领域:
- 生物识别信息 生物识别信息
- 人与计算机的交互
- 步态分析 步态分析
背景情况:
- 脚步识别是一种新兴的生物识别技术.
- 与已建立的生物识别相比,共变量对脚步记录的影响还不太清楚.
- 了解这些影响对于开发强大的脚步识别系统至关重要.
研究的目的:
- 调查内部和外部共变量对空间和时间足迹特征的影响.
- 应用无监督层次聚类 (HCA) 来识别基于这些特征的独特步态模式.
- 为了确定体重,年龄,种族,性别,鞋类和步行速度等因素如何影响脚步数据.
主要方法:
- 利用来自二十个个体的脚步压力模式的无监督层次聚类 (HCA).
- 分析了空间表示 (峰值压力图像) 和时间表示 (地面反应力和压力中心时间序列).
- 采用了22个集群有效性指数,以确保强大的集群技术.
主要成果:
- 在使用HCA的空间和时间步态表示中确定了两个不同的集群.
- 发现体重,年龄,种族和鞋类是这些群体的可区分因素.
- 观察到的与性别和步行速度相关的趋势仅限于步行模式的时间域内.
结论:
- 共变量显著影响脚步生物识别数据,创造出不同的步态集群.
- 脚步生物识别系统可以潜在地使用共变量信息作为软生物识别来增强识别.
- 缓解策略可能是必要的,以解决模型偏差,并改善脚步识别系统的概括性.
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