Human-Computer Interaction Behavior and Intention Prediction Model Based on Eye Movement Characteristics

LIANG Yong-qiang; WANG Wei; QU Jue; YANG Jie; LIU Xiao-wei

doi:10.3969/j.issn.0372-2112.2018.12.024

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Human-Computer Interaction Behavior and Intention Prediction Model Based on Eye Movement Characteristics

更新时间：2025-07-16

- Human-Computer Interaction Behavior and Intention Prediction Model Based on Eye Movement Characteristics
- Acta Electronica Sinica Vol. 46, Issue 12, Pages: 2993-3001(2018)
- 作者机构：
  
  1. 空军工程大学防空反导学院,陕西,西安,710051
  2. 西北工业大学航空学院,陕西,西安,710072
  3. 空军工程大学防空反导学院,陕西,西安,710051
  4. 西北工业大学航空学院,陕西,西安,710072
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51675530)
- DOI：10.3969/j.issn.0372-2112.2018.12.024
  CLC： TP391.4
- Published Online：25 December 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
LIANG Yong-qiang, WANG Wei, QU Jue, et al. Human-Computer Interaction Behavior and Intention Prediction Model Based on Eye Movement Characteristics[J]. Acta Electronica Sinica, 2018, 46(12): 2993-3001.
DOI：

LIANG Yong-qiang, WANG Wei, QU Jue, et al. Human-Computer Interaction Behavior and Intention Prediction Model Based on Eye Movement Characteristics[J]. Acta Electronica Sinica, 2018, 46(12): 2993-3001. DOI： 10.3969/j.issn.0372-2112.2018.12.024.

摘要

针对自适应人机界面对用户行为意图预测的需求，提出一种基于眼动特征的人机交互行为分类及意图预测方法.通过建立简化的界面模型，将用户的行为意图分为5类，设计视觉交互实验收集相关行为意图状态下的眼动特征数据，利用SVM（Support Vector Machine）算法建立分类预测模型，结合差异性分析方法选取眼动特征分量，最终确定连续3个采样注视点的位置

坐标、

坐标、注视时间、眼跳幅度以及瞳孔直径共15个分量作为特征参数可以获得较好的预测效果，其预测精度可达90%以上.

Abstract

Aiming at the demand of predicting adaptive interface user's intention

This paper presents a method of human-computer interaction behavior classification and intention prediction based on eye movement characteristics. By establishing a simplified interface model

the user's operating behavior is divided into 5 categories

design visual interaction experiment to collect the relevant states' eye movement data. The SVM (Support Vector Machine) algorithm is used to establish the classification prediction model

combined with the difference analysis method to select the eye movement feature component. Finally

the position

coordinate

the position

coordinate

the gaze time

the eye jump amplitude and the pu

pil diameter of the 3 consecutive sampling fixation points can be used as the characteristic parameters to obtain the better prediction effect

and the prediction accuracy can reach more than 90%.

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