Effectiveness of a time to fixate for fitness to drive evaluation in neurological patients

TL;DR

This study introduces an automated method to measure time to fixate (TTF) using eye-tracker data and YOLO object detection in neurological patients, aiding fitness-to-drive assessments with promising results.

Contribution

The paper presents a novel automated approach combining eye-tracking and YOLO for TTF calculation, improving assessment accuracy in neurological driving evaluations.

Findings

Efficient TTF computation using YOLO object detector.

Significant TTF differences between fit and unfit drivers.

No influence of TTC, IGD, or speed on TTF results.

Abstract

We present a method to automatically calculate time to fixate (TTF) from the eye-tracker data in subjects with neurological impairment using a driving simulator. TTF presents the time interval for a person to notice the stimulus from its first occurrence. Precisely, we measured the time since the children started to cross the street until the drivers directed their look to the children. From 108 neurological patients recruited for the study, the analysis of TTF was performed in 56 patients to assess fit-, unfit-, and conditionally-fit-to-drive patients. The results showed that the proposed method based on the YOLO (you only look once) object detector is efficient for computing TTFs from the eye-tracker data. We obtained discriminative results for fit-to-drive patients by application of Tukey's honest significant difference post hoc test (p < 0.01), while no difference was observed between conditionally-fit and unfit-to-drive groups (p = 0.542). Moreover, we show that time-to-collision (TTC), initial gaze distance (IGD) from pedestrians, and speed at the hazard onset did not influence the result, while the only significant interaction is among fitness, IGD, and TTC on TTF. Obtained TTFs are also compared with the perception response times (PRT) calculated independently from eye-tracker data and YOLO. Although we reached statistically significant results that speak in favor of possible method application for assessment of fitness to drive, we provide detailed directions for future driving simulation-based evaluation and propose processing workflow to secure reliable TTF calculation and its possible application in for example psychology and neuroscience.