Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146011
Title: Hierarchical eye-tracking data analytics for human fatigue detection at a traffic control center
Authors: Li, Fan
Chen, Chun-Hsien
Xu, Gangyan
Khoo, Li-Pheng
Keywords: Engineering
Issue Date: 2020
Source: Li, F., Chen, C.-H., Xu, G., & Khoo, L.-P. (2020). Hierarchical eye-tracking data analytics for human fatigue detection at a traffic control center. IEEE Transactions on Human-Machine Systems, 50(5), 465-474. doi:10.1109/THMS.2020.3016088
Journal: IEEE Transactions on Human-Machine Systems 
Abstract: 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.
URI: https://hdl.handle.net/10356/146011
ISSN: 2168-2305
DOI: 10.1109/THMS.2020.3016088
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/THMS.2020.3016088
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Journal Articles

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