Journal article
Yuxin Cai, Stephanie C. Payne, Farzan Sasangohar, Ranjana K. Mehta
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2026
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2026
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APA
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Cai, Y., Payne, S. C., Sasangohar, F., & Mehta, R. K. (2026). A {Knowledge}-{Based} {Recommender} {System} for {Personalized} {Fatigue} {Mitigation} {Strategies} in {Offshore} {Workers}. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/10711813261498763
Chicago/Turabian
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Cai, Yuxin, Stephanie C. Payne, Farzan Sasangohar, and Ranjana K. Mehta. “A {Knowledge}-{Based} {Recommender} {System} for {Personalized} {Fatigue} {Mitigation} {Strategies} in {Offshore} {Workers}.” Proceedings of the Human Factors and Ergonomics Society Annual Meeting (2026).
MLA
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Cai, Yuxin, et al. “A {Knowledge}-{Based} {Recommender} {System} for {Personalized} {Fatigue} {Mitigation} {Strategies} in {Offshore} {Workers}.” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2026, doi:10.1177/10711813261498763.
BibTeX Click to copy
@article{cai2026a,
title = {A {Knowledge}-{Based} {Recommender} {System} for {Personalized} {Fatigue} {Mitigation} {Strategies} in {Offshore} {Workers}},
year = {2026},
journal = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},
doi = {10.1177/10711813261498763},
author = {Cai, Yuxin and Payne, Stephanie C. and Sasangohar, Farzan and Mehta, Ranjana K.}
}
Abstract
Fatigue is a major safety concern in high-risk industries and can result from insufficient sleep, prolonged mental workload, and sustained physical exertion. While existing fatigue management approaches often focus on scheduling or generic interventions, they rarely provide personalized recommendations tailored to a worker’s current fatigue condition. To address this gap, a personalized fatigue mitigation recommendation system was designed to support fatigue risk management in offshore oil and gas extraction (OGE) operations. The system integrates research evidence and offshore expert consensus within a multi-criteria decision-making framework to generate individualized fatigue mitigation strategies based on workers’ fatigue profiles. The system considers four fatigue-related indicators: mental exhaustion, physical exertion, perceived sleepiness, and sleep loss. The recommendation system was applied to a real-world offshore fatigue dataset containing over 1,800 fatigue records collected from workers. Results demonstrated the feasibility of generating context-aware and personalized recommendations across a wide range of fatigue states.