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用细分线性回归来分析现实环境中的眼动.

Kritika Johari1, Rishabh Bhardwaj2, Jung-Jae Kim3

  • 1Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore.

Computers in biology and medicine
|April 10, 2024
PubMed
概括
此摘要是机器生成的。

改进的天真细分线性回归 (INSLR) 通过否定视线数据和最大限度地减少预测错误来增强眼动分析. 这种方法显著提高了使用网络摄像头的真实应用程序的准确性.

关键词:
眼球运动分析分析眼球追踪器 眼球追踪器分段线性回归的细分线性回归.

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科学领域:

  • 神经科学和认知科学 神经科学和认知科学
  • 计算机视觉和信号处理

背景情况:

  • 眼动分析对于理解感知,认知和行为至关重要.
  • 现实世界的目光数据通常很,阻碍了对缓慢的短暂变化的精确分析.
  • 现有的方法与低精度设备和不受控制的环境作斗争.

研究的目的:

  • 提出一个改进的天真细分线性回归 (INSLR) 算法,用于强大的眼动分析.
  • 在不受控制的环境中,应对视线毁和预测错误的挑战.
  • 为了提高眼运动事件分类和注意力变化检测的准确性.

主要方法:

  • 开发了INSLR,一种以零碎线性函数 (PLF) 接近视线坐标的方法.
  • 采用了一个假设集群算法,具有最小方位匹配得分和k-平均距离,用于假设估计.
  • 过的假设使用预定义的值来完善凝视信号近似.

主要成果:

  • 在拒绝噪音眼动数据集方面,INSLR始终优于基线NSLR.
  • 在71.1%的样本中,低精度设备 (网络摄像头) 的目光预测错误被最小化.
  • 通过提高NSLR-HMM眼运动事件分类器的精度,验证了改进的无噪声.

结论:

  • 在现实世界,不受控制的环境中,INSLR为眼动分析提供了显著的进步.
  • 该方法有效地消除了目光数据,并减少了预测错误,即使使用具有成本效益的硬件.
  • 通过INSLR,可以更好地检测注意力和眼运动事件的微妙变化.