论文标题

固定时间进行适应性来推动神经病患者评估的有效性

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

论文作者

Miljković, Nadica, Sodnik, Jaka

论文摘要

我们提出了一种使用驱动模拟器中神经系统损伤的受试者的眼球数据中自动计算固定时间(TTF)的方法。 TTF介绍了一个人从首次发生时注意到刺激的时间间隔。确切地说,我们测量了自孩子们开始过马路以来的时间,直到驾驶员将外观指向孩子们。从108名招募研究的神经系统患者中,对56名患者进行了TTF的分析,以评估适合,不适合和有条件拟合的患者。结果表明,基于YOLO(您只看一次)对象检测器的提出方法有效地从眼线跟踪数据计算TTF。我们通过应用Tukey事后检验的诚实差异(P <0.01)获得了适合驱动的患者的区分结果,而有条件拟合和不合适的驱动组之间没有观察到差异(P = 0.542)。此外,我们表明,碰撞时间(TTC),行人的初始凝视距离(IGD)以及危险发作处的速度并不影响结果,而唯一的显着相互作用是在TTF上的健身,IgD和TTC中。还将获得的TTF与独立于眼球数据和Yolo独立计算的感知响应时间(PRT)进行了比较。尽管我们达到了统计学上的显着结果,这些结果支持可能的方法来评估适应性驱动力,但我们为将来的基于驾驶模拟的评估提供了详细的方向,并提出了处理工作流程,以确保可靠的TTF计算及其在心理学和神经科学中的可能应用。

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.

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