TY - JOUR
T1 - Hierarchical Eye-Tracking Data Analytics for Human Fatigue Detection at a Traffic Control Center
AU - Li, Fan
AU - Chen, Chun Hsien
AU - Xu, Gangyan
AU - Khoo, Li Pheng
N1 - Funding Information:
Manuscript received February 12, 2019; revised July 12, 2019, January 13, 2020, and April 14, 2020; accepted July 18, 2020. Date of publication August 28, 2020; date of current version September 15, 2020. This work was supported in part by the Singapore Maritime Institute Research Project under Grant SMI-2014-MA-06, in part by the National Natural Science Foundation of China under Grant 71804034, in part by the Research Foundation of STIC under Grant JCYJ20180306171958907, in part by the CCF-Tencent Open Research Fund, and in part by the Startup Foundation for High-Level Talents of Harbin Institute of Technology, Shenzhen. This article was recommended by Associate Editor L. Lin. (Corresponding author: Gangyan Xu.) Fan Li, Chun-Hsien Chen, and Li-Pheng Khoo are with the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 546080, Singapore (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Eye-tracking-based human fatigue detection at traffic control centers suffers from an unavoidable problem of low-quality eye-tracking data caused by noisy and missing gaze points. In this article, the authors conducted pioneering work by investigating the effects of data quality on eye-tracking-based fatigue indicators and by proposing a hierarchical-based interpolation approach to extract the eye-tracking-based fatigue indicators from low-quality eye-tracking data. This approach adaptively classified the missing gaze points and hierarchically interpolated them based on the temporal-spatial characteristics of the gaze points. In addition, the definitions of applicable fixations and saccades for human fatigue detection is proposed. Two experiments are conducted to verify the effectiveness and efficiency of the method in extracting eye-tracking-based fatigue indicators and detecting human fatigue. The results indicate that most eye-tracking parameters are significantly affected by the quality of the eye-tracking data. In addition, the proposed approach can achieve much better performance than the classic velocity threshold identification algorithm (I-VT) and a state-of-the-art method (U'n'Eye) in parsing low-quality eye-tracking data. Specifically, the proposed method attained relatively stable eye-tracking-based fatigue indicators and reported the highest accuracy in human fatigue detection. These results are expected to facilitate the application of eye movement-based human fatigue detection in practice.
AB - Eye-tracking-based human fatigue detection at traffic control centers suffers from an unavoidable problem of low-quality eye-tracking data caused by noisy and missing gaze points. In this article, the authors conducted pioneering work by investigating the effects of data quality on eye-tracking-based fatigue indicators and by proposing a hierarchical-based interpolation approach to extract the eye-tracking-based fatigue indicators from low-quality eye-tracking data. This approach adaptively classified the missing gaze points and hierarchically interpolated them based on the temporal-spatial characteristics of the gaze points. In addition, the definitions of applicable fixations and saccades for human fatigue detection is proposed. Two experiments are conducted to verify the effectiveness and efficiency of the method in extracting eye-tracking-based fatigue indicators and detecting human fatigue. The results indicate that most eye-tracking parameters are significantly affected by the quality of the eye-tracking data. In addition, the proposed approach can achieve much better performance than the classic velocity threshold identification algorithm (I-VT) and a state-of-the-art method (U'n'Eye) in parsing low-quality eye-tracking data. Specifically, the proposed method attained relatively stable eye-tracking-based fatigue indicators and reported the highest accuracy in human fatigue detection. These results are expected to facilitate the application of eye movement-based human fatigue detection in practice.
KW - Eye movement
KW - hierarchical-based interpolation
KW - human fatigue
KW - traffic management
UR - http://www.scopus.com/inward/record.url?scp=85090194152&partnerID=8YFLogxK
U2 - 10.1109/THMS.2020.3016088
DO - 10.1109/THMS.2020.3016088
M3 - Journal article
AN - SCOPUS:85090194152
SN - 2168-2291
VL - 50
SP - 465
EP - 474
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
IS - 5
M1 - 9180051
ER -