Summary
- Experiments were conducted on 43 healthy volunteers to measure changes in pupil size and gaze direction by inducing changes in pupil size and position using eye tracking methods in a closed-eye setting.
- The experimental setup included a chinrest, LED illumination, a SWIR camera, and a computer screen, with participants sitting 50 cm from the screen in a dark room with constant ambient light.
- Control experiments were conducted to estimate the effect of confounding factors like screen light interference, differences in pupil diameter when both eyes are open, and the effect of eyelid stretching on measurements.
- Data analysis techniques like DeepLabCut, U-NET neural net analysis, and statistical tests were used to analyze the data and validate the measurements of pupil size dynamics and gaze direction estimation.
- Statistically significant findings were reported, including the ability to capture pupillary light reflex dynamics through closed eyes and accurately estimate changes in gaze direction using deep learning analysis of SWIR data.
Researchers at Tel Aviv University conducted a study involving 43 healthy volunteers to explore changes in pupil size and gaze direction. The study, approved by the university’s ethical committee, focused on measuring pupil parameters by inducing changes in pupil size and position, utilizing eye tracking technology.
In the experiments, participants were asked to sit still with one eye open and the other closed while holding their eyelid near the lash line. This method was based on the synchronization of pupil size and gaze in healthy individuals. The setup included a chinrest, LED illumination, a SWIR camera, and a computer screen placed at a specific distance and angle from the participants.
The main experiment involved assessing changes in pupil size triggered by light stimuli on a computer screen. Participants underwent ten visual stimulation trials interleaved with rest periods. Another experiment focused on tracking changes in gaze direction using crosshair fixation targets on the screen.
Control experiments were also conducted to account for confounding factors such as screen light interference and differences in pupil diameter due to closed eyelids. Data analysis involved methods such as deep learning analysis and a “fixed circle” approach to track pupil dynamics and gaze direction accurately.
The study aimed to validate the findings by analyzing data from both open and closed eyes. The researchers excluded sessions with excessive movements and used statistical analysis to assess the accuracy of their methods.
Overall, the research findings provide insights into how changes in pupil size and gaze direction can be accurately measured through closed eyelids. The study’s robust methodology and control experiments ensure the reliability of the results, contributing to a better understanding of eye tracking technology.
Source link
Ophthalmology, Critical Care