Vigilance Decrement Measurement, Prediction, and Intervention Design in Safety-Critical Environments

Abstract

Vigilance is defined as the ability to remain attentive and alert over a sustained period of time, particularly toward a specific task. The deterioration of vigilance, also known as vigilance decrement, is often experienced by those completing tasks over an extended period of time. In high-risk domains, such as healthcare and driving, measuring vigilance decrement is particularly important given the resulting safety impacts. However, most research studies primarily use behavioral responses during vigilance tasks to assess vigilance levels (e.g., response time, hit rate). While these measures have proven to be effective, they are limited to post-hoc analyses only. Given recent advances in physiological sensing tools and predictive modeling strategies, it is important to also consider contextual factors and physiological indicators, allowing for more robust, continuous, and objective evaluation of vigilance decrement. For effective prevention and resolution to cases of vigilance decrement in safety-critical settings, interventions must be designed to be proactive and sustainable. This dissertation uses a combination of scoping reviews, healthcare-based studies, and a driving simulator study to answer the following research questions: 1) What contextual or contributing factors, together, affect vigilance levels in safety-critical settings?, 2) What combination of physiological measures can be used to monitor vigilance?, 3) What modeling features and strategies can be used to predict vigilance decrement?, and 4) What types of interventions can be used to maintain vigilance in a sustainable manner? Findings across the healthcare and driving studies revealed that vigilance is best predicted through a hybrid approach combining contextual factors and multi-sensor physiological measures. In the healthcare domain, sleep-related and environmental factors were significant predictors of vigilance, while heart rate, electrodermal activity, skin temperature, and eye movement-related metrics emerged as key physiological indicators. In the driving domain, driver experience, distraction susceptibility, and workload were the most influential contextual factors, while eye fixations toward a virtual meeting-based secondary task serving as the primary physiological predictor. Continuous measures of driving behavior were used as effective substitutes for peripheral physiological sensing. In the healthcare domain, probabilistic models with temporal elements, such as Dynamic Bayesian Networks, performed well for longer-duration vigilance tasks. In the driving domain, simpler linear models were most effective for single, critical event-based vigilance assessments. A scoping review of 52 studies identified 18 vigilance intervention strategies where sleep/breaks, ingestion, meditation/mindfulness, and task context changes represented the most sustainable options for real-world deployment. In the driving study, an emergency-style warning intervention produced significantly faster response times during the takeover event compared to the less urgent planned-style warning, demonstrating that salient, system-integrated alerts can serve as effective and sustainable interventions for enhancing vigilance prior to safety-critical events in semi-autonomous vehicles.