The next generation of fatigue prediction models: evaluating current trends in biomathematical modelling

Abstract

Biomathematical models (BMMs) are parametric models that quantitatively predict fatigue and are routinely implemented in fatigue risk management systems in increasingly diverse workplaces. There have been consistent calls for an improved ‘next generation’ of BMMs that provide more accurate and targeted predictions of human fatigue. This article examines the core characteristics of next-generation advancements in BMMs, including tailoring with field data, individual-level parameter tuning and real-time fatigue prediction, extensions to account for additional factors that influence fatigue, and emerging nonparametric methodologies that may augment or provide alternatives to BMMs. Examination of past literature and quantitative examples suggests that there are notable challenges to advancing BMMs beyond their current applications. Adoption of multi-model frameworks, including quantitative joint modelling and machine-learning, was identified as crucial to next-generation models. We close with general recommendations for researchers, practitioners, and model developers, including focusing research efforts on understanding the cognitive dynamics underpinning fatigue-related vigilance decrements, applying emerging dynamic modelling methods to fatigue data from field settings, and improving the adoption of open scientific practices in fatigue research.

Keywords:

• Cognitive modelling
• alertness
• performance
• sleepiness
• prediction

Data availability statement

Full information pertaining to analyses conducted in the manuscript, including the code used to conduct modelling, are stored in the manuscript’s open science framework repository and are accessible via https://osf.io/yurvx/

Disclosure statement

None

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was supported by funding from the Australian Defence Science and Technology Group under MyIP:9079; Forrest Research Foundation Prospect Fellowship awarded to Micah K. Wilson.