基于用户自定义兴趣区的飞行员眼动数据可视分析方法

doi:10.11772/j.issn.1001-9081.2019030494

计算机应用 ›› 2019, Vol. 39 ›› Issue (9): 2683-2688.DOI: 10.11772/j.issn.1001-9081.2019030494

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于用户自定义兴趣区的飞行员眼动数据可视分析方法

贺怀清, 郑立源, 刘浩翰, 张昱旻

中国民航大学计算机科学与技术学院, 天津 300300

收稿日期:2019-03-25 修回日期:2019-05-08 出版日期:2019-09-10 发布日期:2019-05-14
通讯作者: 郑立源
作者简介:贺怀清(1969-),女,吉林白山人,教授,博士,CCF会员,主要研究方向:图形图像与可视分析;郑立源(1993-),男,河南周口人,硕士研究生,主要研究方向:图形图像与可视分析;刘浩翰(1966-),男,黑龙江富锦人,副教授,硕士,主要研究方向:图形图像与可视分析;张昱旻(1996-),女,河北邯郸人,硕士研究生,主要研究方向:图形图像与可视分析。
基金资助:
国家自然科学基金资助项目（U1333110）。

Visual analysis method for pilot eye movement data based on user-defined interest area

HE Huaiqing, ZHENG Liyuan, LIU Haohan, ZHANG Yumin

College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Received:2019-03-25 Revised:2019-05-08 Online:2019-09-10 Published:2019-05-14
Supported by:
This work is partially supported by the National Natural Science Foundation of China (U1333110).

摘要/Abstract

摘要：

针对传统基于兴趣区的可视化方法在分析飞行员眼动数据过程中无法关注细节的问题，提出了一种基于用户自定义兴趣区的眼动数据可视分析方法。首先，根据具体的分析任务，引入对任务背景图像的自我划分和定义；然后，在此基础上，结合多种辅助视图和交互手段，设计并实现了面向飞行员培训的眼动数据可视分析系统，帮助分析人员分析不同的飞行员之间的眼动差异，最后通过案例分析，证明了可视分析方法的有效性和分析系统的实用性。实验结果表明，较传统方法来说，所提方法增加了分析人员在分析过程中的主动性，在整体和局部方面，支持分析人员对任务背景进行细节的探索，增加了分析人员分析数据的多角度性，让分析人员能够结合整体发现飞行学员在训练过程中认知困难的部分，进而制定更有针对性、更有效的训练课程。

关键词: 飞行员, 眼动数据, 数据可视化, 可视分析, 兴趣区, 多视图

Abstract:

Focused on the issue that the traditional interest area based visualization method can not pay attention to the details in the process of analyzing pilot eye movement data, a visual analysis method of eye movement data based on user-defined interest area was proposed. Firstly, according to the specific analysis task, the self-divison and self-definition of the background image of the task were introduced. Then, multiple auxiliary views and interactive approaches were combined, and an eye movement data visual analysis system for pilot training was designed and implemented to help analysts analyze the difference of eye movement between different pilots. Finally, through case analysis, the effectiveness of the visual analysis method and the practicability of the analysis system were proved. The experimental results show that compared with the traditional method, in the proposed method, the analysts' initiative in the analysis process is increased. The analysts are allowed to explore the local details of the task background in both global and local aspects, making the analysts' analyze the data in multi-angle; the analysts are allowed find the flight students' cognitive difficulties in the training process as a whole, so as to develop more targeted and more effective training courses.

Key words: pilot, eye movement data, data visualization, visual analytics, Area Of Interest (AOI), multi-view

中图分类号:

TP391.9

贺怀清, 郑立源, 刘浩翰, 张昱旻. 基于用户自定义兴趣区的飞行员眼动数据可视分析方法[J]. 计算机应用, 2019, 39(9): 2683-2688.

HE Huaiqing, ZHENG Liyuan, LIU Haohan, ZHANG Yumin. Visual analysis method for pilot eye movement data based on user-defined interest area[J]. Journal of Computer Applications, 2019, 39(9): 2683-2688.

参考文献

[1] SHAPPELL S, DETWILER C, HOLCOMB K, et al. Human error and commercial aviation accidents:an analysis using the human factors analysis and classification system[J]. Human Factors, 2007, 49(2):227-242.
[2] Federal Aviation Administration (FAA). 14 CFR part 121 qualification, service, and use of crewmembers and aircraft dispatchers; final rule; federal register[EB/OL].[2018-11-02]. http://www.gpo.gov/fdsys/granule/FR-2013-11-12/2013-26845.
[3] British European Airways (BEA). Study on aeroplane state awareness during go-around[EB/OL].[2018-11-02]. https://www.skybrary.aero/bookshelf/books/2404.pdf.
[4] LEFRANCOIS O, MATTON N, YVES G, et al. The role of pilots' monitoring strategies in flight performance[EB/OL].[2018-11-02]. http://oatao.univ-toulouse.fr/16173/1/Lefrancois_16173.pdf.
[5] 柳忠起,袁修干,樊瑜波,等.模拟飞机着陆飞行中专家和新手眼动行为的对比[J].航天医学与医学工程,2009,22(5):358-361.(LIU Z Q, YUAN X G, FAN Y B, et al. Comparison of expert and novice eye movement behaviors during simulated landing flight[J]. Space Medicine and Medical Engineering, 2009, 22(5):358-361.)
[6] 孙瑞山,汪苧,董屾.基于非接触式眼动仪的模拟飞行中注视分析[J].人类工效学,2013,19(4):51-55.(SUN R S, WANG N, DONG S. Simulated in-flight gaze analysis based on non-contact eye tracker[J]. Chinese Journal of Ergonomics, 2013, 19(4):51-55.)
[7] REYNAL M, COLINEAUX Y, VERNAY A, et al. Pilot flying vs. pilot monitoring during the approach phase:an eye-tracking study[C]//Proceedings of the 2016 International Conference on Human-Computer Interaction in Aerospace. New York:ACM, 2016:Article No. 7.
[8] DEHAIS F, BEHREND J, PEYSAKHOVICH V, et al. Pilot flying and pilot monitoring's aircraft state awareness during go-around execution in aviation:a behavioral and eye tracking study[J]. The International Journal of Aerospace Psychology, 2017, 27(1/2):15-28.
[9] 白杰,葛祥雨,杨坤.飞行员探测跑道入侵的眼动模式分析[J].航天医学与医学工程, 2017,30(5):327-332.(BAI J, GE X Y, YANG K. Analysis of eye movement patterns in pilot's detection of runway incursion[J]. Space Medicine and Medical Engineering, 2017, 30(5):327-332.)
[10] 程时伟,孙凌云.眼动数据可视化综述[J].计算机辅助设计与图形学学报,2014,26(5):698-707.(CHENG S W, SUN L Y. A survey on visualization for eye tracking data[J]. Journal of Computer-Aided Design and Computer Graphics, 2014, 26(5):698-707.)
[11] RICHARDSON D C, DALE R. Looking to understand:the coupling between speakers' and listeners' eye movements and its relationship to discourse comprehension[J]. Cognitive Science,2005, 29(6):1045-1060.
[12] STELLMACH S, NACKE L, DACHSELT R. Advanced gaze visualizations for three-dimensional virtual environments[C]//Proceedings of the 2010 Symposium on Eye-tracking Research and Applications. New York:ACM, 2010:109-112.
[13] KURZHALS K, HLAWATSCH M, HEIMERL F, et al. Gaze stripes:image-based visualization of eye tracking data[J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):1005-1014.
[14] BURCH M, KULL A, WEISKOPF D. AOI rivers for visualizing dynamic eye gaze frequencies[C]//Proceedings of the 15th Eurographics Conference on Visualization. Chichester, UK:the Eurographs Association and John Wiley & Sons, 2013:281-290.
[15] BLASCHECK T, SCHWEIZER M, BECK F, et al. Visual comparison of eye movement patterns[J]. Computer Graphics Forum, 2017, 36(3):87-97.

基于用户自定义兴趣区的飞行员眼动数据可视分析方法

Visual analysis method for pilot eye movement data based on user-defined interest area

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