부산대학교 도서관

Hypovigilance represents a major contributor to accidents. In operational contexts, the burden of monitoring/managing vigilance often rests on operators. Recent advances in sensing technologies allow for the development of psychophysiology‐based (hypo)vigilance prediction models. Still, these models remain scarcely applied to operational situations and need better understanding. The current scoping review provides a state of knowledge regarding psychophysiological models of hypovigilance detection. Records evaluating vigilance measuring tools with gold standard comparisons and hypovigilance prediction performances were extracted from MEDLINE, PsychInfo, and Inspec. Exclusion criteria comprised aspects related to language, non‐empirical papers, and sleep studies. The Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS) and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were used for bias evaluation. Twenty‐one records were reviewed. They were mainly characterized by participant selection and analysis biases. Papers predominantly focused on driving and employed several common psychophysiological techniques. Yet, prediction methods and gold standards varied widely. Overall, we outline the main strategies used to assess hypovigilance, their principal limitations, and we discuss applications of these models. We conducted a scoping review on psychophysiological models of hypogivilant state. Studies reviewed relied either on diagnostic or machine learning approaches. Induction methods and levels of hypovigilance varied importantly between studies. Hypovigilance detection mainly relied on electrocardiography (ECG),photoplethysmography (PPG), electroencephalography (EEG), electrooculography (EOG), and eye tracking. Results can help to develop user‐tailored systems to mitigate hypovigilance. [ABSTRACT FROM AUTHOR]