Related Experiment Video
Updated: May 8, 2026
PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability
Published on: April 12, 2024
Assessment of drowsiness based on ocular parameters detected by infrared reflectance oculography
Clare Anderson1, Anne-Marie Chang, Jason P Sullivan
1Division of Sleep Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. clare.anderson@monash.edu
Study Objectives:
Numerous ocular parameters have been proposed as reliable physiological markers of drowsiness. A device that measures many of these parameters and then combines them into a single metric (the Johns Drowsiness Scale [JDS]) is being used commercially to assess drowsiness in professional drivers. Here, we examine how these parameters reflect changes in drowsiness, and how they relate to objective and subjective indices of the drowsy state in a controlled laboratory setting.
Design:
A within subject prospective study.
Participants:
29 healthy adults (18 males; mean age 23.3 ± 4.6 years; range 18-34 years).
Interventions:
N/A.
Measurements And Results:
Over the course of a 30-h extended wake vigil under constant routine (CR) conditions, participants were monitored using infrared reflectance oculography (Optalert) and completed bi-hourly neurobehavioral tests, including the Karolinska Sleepiness Scale (KSS) and Psychomotor Vigilance Task (PVT). Ocular-defined increases in drowsiness were evident with extended time awake and during the biological night for all ocular parameters; JDS being the most sensitive marker of drowsiness induced by sleep regulatory processes (p < 0.0001). In addition, the associations between JDS in the preceding 10-min period and subsequent PVT lapses and KSS were stronger (AUC 0.74/0.80, respectively) than any other ocular metric, such that PVT lapses, mean response time (RT), and KSS increased in a dose-response manner as a function of prior JDS score (p < 0.0001).
Conclusions:
Ocular parameters captured by infrared reflectance oculography detected fluctuations in drowsiness due to time awake and during the biological night. The JDS outcome was the strongest predictor of drowsiness among those tested, and showed a clear association to objective and subjective measures of drowsiness. Our findings indicate this real-time objective drowsiness monitoring system is an effective tool for monitoring changes in alertness and performance along the alert-drowsy continuum in a controlled laboratory setting.
