research-article

Monitoring Interface Design Based on Real-time Fatigue Detection

Authors:

Ziyu Yao,

Xiaozhou Zhou,

Hao Qin,

Weiye XiaoAuthors Info & Claims

BDIOT '20: Proceedings of the 2020 4th International Conference on Big Data and Internet of Things

Pages 49 - 53

https://doi.org/10.1145/3421537.3421556

Published: 05 October 2020 Publication History

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Abstract

In video monitoring tasks, surveillance personnel are required to monitor video content over an extended period of time. When workers get into fatigue state, their working efficiency and cognitive performance tend to decrease, and they are more likely to get distracted. Therefore, in the context of prolonged and monotonous vigilance task, workers' cognitive load should be lowered and their ability of concentrating should be improved as an effort to sustain their work performance. This study combined facial recognition techniques which detect workers' fatigue state with computer camera, and improved user interface to boost workers' working efficiency. This paper provides a new idea of the design of self-adaptive user interface based on fatigue detection.

References

[1]

Mcfadden, S. M., Vimalachandran, A., & Blackmore, E. (2007). Factors affecting performance on a target monitoring task employing an automatic tracker. Ergonomics. 47, 3 (Feb. 2007) 257--280. DOI=http://doi.org/10.1080/00140130310001629748.

Crossref

Google Scholar

[2]

Mckinley, R. A., Mcintire, L. K., Schmidt, R., Repperger, D. W., and Caldwell, J. A. 2011. Evaluation of Eye Metrics as a Detector of Fatigue. Human Factors: The Journal of Human Factors and Ergonomics Society. 53, 4 (Aug. 2011), 403--414. DOI= http://doi.org/10.1177/0018720811411297.

Crossref

Google Scholar

[3]

Cameron, C. 2007. Fatigue Problems in Modern Industry. Ergonomics. 14, 6 (Nov. 2007), 713--720. DOI=http://doi.org/10.1080/00140137108931294.

Crossref

Google Scholar

[4]

Lafeber, H., van Oostendorp, H., and Lindenberg, J. 2009. Eye Movement as Indicators of Mental Workload to Trigger Adaptive Automation. In Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: 5th International Conference, FAC 2009 Held as Part of HCI International 2009 (San Diego, CA, USA, July 19-24, 2009). DOI= http://doi.org/10.1007/978-3-642-02812-0_26.

Crossref

Google Scholar

[5]

Van Orden, K. F., Jung, T.-P., and Makeig, S. 2000. Combined eye activity measures accurately estimate changes in sustained visual task performance. Biological Psychology. 52, 3 (Mar. 2000), 221--240. DOI=http://doi.org/10.1016/S0301-0511(99)00043-5.

Crossref

Google Scholar

[6]

Wilson, G. F. 2002. An Analysis of Mental Workload in Pilots During Flight Using Multiple Psychophysiological Measures. The International Journal of Aviation Psychology. 12, 1 (Jan. 2002), 3--18. DOI=http://doi.org/10.1207/S153271081JAP1201_2.

Crossref

Google Scholar

[7]

Nie, Baisheng, Huang, Xin, Chen, Yang, Li, Anjin, Zhang, Ruming, and Huang, Jinxin. 2017. Experimental study on visual detection for fatigue of fixed-position staff. Applied Ergonomics. 65 (Nov. 2017), 1--11. DOI=http://doi.org/10.1016/j.apergo.2017.05.010.

Crossref

Google Scholar

[8]

Jackson, M. L., Raj, S., Croft, R. J., Hayley, A. C., Downey, L. A., Kennedy, G. A., and Howard, M. E. 2016. Slow eyelid closure as a measure of driver drowsiness and its relationship to performance. Traffic Injury Prevention. 17, 3 (Apr. 2016), 251--257. DOI=http://doi.org/10.1080/15389588.2015.1055327.

Crossref

Google Scholar

[9]

Mandal, B., Li, Liyuan, Wang, and Lin, Jie. 2017. Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State. IEEE Transactions on Intelligent Transportation Systems. 18, 3 (Mar, 2017), 545--557. http://doi.org/10.1109/TITS.2016.2582900.

Digital Library

Google Scholar

[10]

Öner, M., Kazanasmaz, T., Leccese, F., Salvadori, G., Ucci, M., and Godefroy, J. 2020. Analysis of the relationship between daylight illuminance and cognitive, affective and physiological changes in visual display terminal workers. Building Services Engineering Research & Technology. 41, 2 (March. 2020), 167--182. DOI=http://doi.org/10.1177/0143624419894441.

Crossref

Google Scholar

[11]

Li, Y.-H., You, F., Chen, K., Huang, L., & Xu, J.-M. 2016. A real-time system for monitoring driver fatigue. Transportation Planning and Technology: Special Issue on Intelligent Transportation Systems, Big Data and Intelligent Technology. 39, 8 (Nov. 2016), 779--790. DOI=http://doi.org/10.1080/03081060.2016.1231897.

Crossref

Google Scholar

Cited By

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Smoliński AForczmański PNowosielski A(2024)Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle UsersElectronics10.3390/electronics1313245713:13(2457)Online publication date: 23-Jun-2024
https://doi.org/10.3390/electronics13132457

Index Terms

Monitoring Interface Design Based on Real-time Fatigue Detection
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. User interface design

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BDIOT '20: Proceedings of the 2020 4th International Conference on Big Data and Internet of Things

August 2020

108 pages

ISBN:9781450375504

DOI:10.1145/3421537

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 October 2020

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Science and Technology on Avionics Integration Laboratory and Aeronautical Science Fund

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BDIOT 2020

BDIOT 2020: 2020 the 4th International Conference on Big Data and Internet of Things

August 22 - 24, 2020

Singapore, Singapore

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Smoliński AForczmański PNowosielski A(2024)Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle UsersElectronics10.3390/electronics1313245713:13(2457)Online publication date: 23-Jun-2024
https://doi.org/10.3390/electronics13132457

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A Real-time Fatigue Driving Detection Method Based on Facial Multi-Feature Fusion

Hardware Design of Fatigue Detection System Based on PERCLOS

Driver fatigue detection based on eye state

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A Real-time Fatigue Driving Detection Method Based on Facial Multi-Feature Fusion

Hardware Design of Fatigue Detection System Based on PERCLOS

Driver fatigue detection based on eye state

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