Individualizing a biomathematical fatigue model with attention data

Fatigue is a problematic factor in many workplace environments, resulting in safety and health risks that require monitoring and management. One means to monitor and manage fatigue is through the use of tools implementing biomathematical fatigue models to create assessment and predictions of operator fatigue based on sleep habits. Unfortunately, these models tend to provide assessments and predictions for an “average” operator given work schedules, lacking individualization. One way in which these models can be individualized is through the use of at-the-moment performance data that can modulate the model estimates. In the current effort, we describe an initial attempt at developing an algorithm to individualize fatigue assessments and predictions from a widely-used biomathematical fatigue model with performance data from a common attention task. We discuss the sleep dataset used for the effort, scaling procedure, and model fitting using a genetic algorithm. We then discuss future directions we will take to further increase the effectiveness and efficiency of the individualization capability and its implications.