基于眼动特征的人机交互行为意图预测模型

doi:10.3969/j.issn.0372-2112.2018.12.024

电子学报 ›› 2018, Vol. 46 ›› Issue (12): 2993-3001.DOI: 10.3969/j.issn.0372-2112.2018.12.024

基于眼动特征的人机交互行为意图预测模型

梁永强¹, 王崴¹, 瞿珏^1,2, 杨洁¹, 刘晓卫¹

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

收稿日期:2017-10-23 修回日期:2018-05-08 出版日期:2018-12-25
- 通讯作者:
- 王崴
- 作者简介:
- 梁永强 男,1994年生于山西石楼.空军工程大学硕士研究生.研究方向为武器装备人机交互设计、自适应人机界面.E-mail:1102268250@qq.com
- 基金资助:
- 国家自然科学基金 (No.51675530）

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

LIANG Yong-qiang¹, WANG Wei¹, QU Jue^1,2, YANG Jie¹, LIU Xiao-wei¹

1.Air and Missile Defense College, Air Force Engineering University. Xi'an, Shaanxi 710051, China;
2.School of Aeronautics, Northwestern Polytechnical University. Xi'an, Shaanxi 710072, China

Received:2017-10-23 Revised:2018-05-08 Online:2018-12-25 Published:2018-12-25
- Corresponding author:
- WANG Wei
- Supported by:
- National Natural Science Foundation of China (No.51675530)

摘要/Abstract

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X坐标、Y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.

关键词: 自适应界面, 眼动特征, 交互意图, 支持向量机

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 X coordinate, the position Y coordinate, the gaze time, the eye jump amplitude and the pupil 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%.

Key words: adaptive interface, eye movement characteristics, intention prediction, SVM

中图分类号:

TP391.4

梁永强, 王崴, 瞿珏, 等. 基于眼动特征的人机交互行为意图预测模型[J]. 电子学报, 2018, 46(12): 2993-3001.

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.

参考文献

[1] 葛列众.工程心理学[M].上海:华东师范大学出版社,2017.163-164.
[2] 宋巍,刘丽珍,王函石.基于兴趣偏好的微博用户性别推断研究[J].电子学报,2016,44(10):2522-2529. SONG Wei,LIU Li-zhen,WANG Han-shi.Userinterface preferences for gender inference on microblog[J].Acta Electronica Sinica,2016,44(10):2522-2529.(in Chinese)
[3] 陈杰,刘学军,李斌,等.一种基于用户动态兴趣和社交网络的微博推荐方法[J].电子学报,2017,45(4):898-905. CHEN Jie,LIU Xue-jun,LI Bin,et.al.Personalized microblogging recommendation based on dynamic interests and social networking of users[J].Acta Electronica Sinica,2017,45(4):898-905.(in Chinese)
[4] Keith Rayner.Eye movements and attention inreading,scene perception,and visual search[J].Quarterly Journal of Experimental Psychology,2009,62(8):1457-506.
[5] Drew T,Evans K,Võ M L,et al.Informatics in radiology:what can you see in a single glance and how might this guide visual search in medical images[J].Radiographics,2013,33(1):263-274
[6] Hasse C,Bruder C.Eye-tracking measurementsand their link to a normative model of monitoring behaviour[J].Ergonomics,2015,58(3):1-13.
[7] Katagiri N,Marumo Y,Tsunashima H.Controllerdesign and evaluation of lane-keeping-assistance system for motorcycles[J].Journal of Mechanical Systems for Transportation & Logistics,2009,2(1):43-54.
[8] 高军峰,司慧芳,余彬,等.基于脑电样本熵的测谎分析[J].电子学报,2017,45(8):1836-1841. Gao Jun-feng,Si Hui-fang,Yu Bin,et.Al.Liedetection analysis based on the sample entropy on EEG[J].Acta Electronica Sinica,2017,45(8):1836-1841.(in Chinese)
[9] Murata A.Improvement of pointing time bypredicting targets in pointing with a PC mouse[J].International Journal of Human computer Interaction,1998,10(1):23-32.
[10] Hertzum M,Hornbã K.The effect of targetprecuing on pointing with mouse and touchpad[J].International Journal of Human computer Interaction,2013,29(5):338-350.
[11] Biswas P,Langdon P M.Multi-modal targetprediction[A].8th International Conference on Universal Access in Human-Computer Interaction[C].Heraklion,Crete,Greec:Springer International Publishing,2014.313-324.
[12] Hsu C K,Lin S C,Li W C.Visual movement andmental-workload for pilot performance assessment[A].International Conference on Engineering Psychology and Cognitive Ergonomics[C].Cham:Springer International Publishing,2015.356-364.
[13] 赵其杰,邵辉,卢建霞.基于头眼行为的交互意图检测方法[J].仪器仪表学报,2014,35(10):2313-2320. Zhao Qi-jie,Shao Hui,Lu Jian-xia.Identificationmethod of interaction intention based on head and eye behaviors[J].Journal of Psychological Science,2014,35(10):2313-2320.(in Chinese)
[14] 卢万譞,贾云得.基于眼动数据的网络搜索行为预测方法[J].北京航空航天大学学报,2015,41(05):904-910. Lu Wan-xuan,Jia Yun-de.Predicting web searchbehavior based on gaze data[J].Journal of Beijing University of Aeronautics and Astronautics,2015,41(05):904-910.(in Chinese)
[15] 操雅琴,郭伏,屈庆星.基于多模式测量的网站用户行为意图预测模型[J].东北大学学报(自然科学版),2014,35(11):1669-1672. Cao Ya-qin,Guo Fu,Qu Qing-xing.Predictionmodels of website users' behavioral intentions based on multi-mode measurement[J].Journal of Northeastern University(Natural Science),2014,35(11):1669-1672.(in Chinese)
[16] Cameirão M S,Faria A L,Paulino T,et al.Theimpact of positive,negative and neutral stimuli in a virtual reality cognitive-motor rehabilitation task:a pilot study with stroke patients[J].Journal of Neuroengineering & Rehabilitation,2016,13(1):70.
[17] 杭久成,何卫平.浅析数控机床触摸屏人机交互界面设计[J].机械制造,2008,(03):23-26. Han Jiu-cheng,He Wei-ping.Analysis onhuman-computer interaction interface design of CNC machine tool[J].Machinery,2008,(03):23-26.(in Chinese)
[18] 李鹏程,张力,戴立操,黄卫刚.核电厂数字化人-机界面特征对人因失误的影响研究[J].核动力工程,2011,32(01):48-52. Li Peng-cheng,Zhang Li,Dai Li-cao,Huang Wei-gang.Effects of digital human-machine interaction characteristics on human error in nuclear plants[J].Nuclear Power Engineering,2011,32(01):48-52.(in Chinese)
[19] 王建峰,王崴,高虹霓,等.威力镜头对雷达界面动态目标点击的可用性研究[J].航天医学与医学工程,2017,30(4):277-282. Wang Jian-feng,Wang Wei,Gao Hong-ni,et.al.Usability study of power-lens for click of dynamic targets in radar interface[J].Space Medicine & Medical Engineering,2017,30(4):277-282.(in Chinese)

基于眼动特征的人机交互行为意图预测模型

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

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