Title: Cannabis Impairment Monitoring Using Objective Eye Tracking Analytics Show Chinese title

Title: 使用客观眼动追踪分析的大麻损害监测

Abstract: The continuing growth in cannabis legalization necessitates the development of rapid, objective methods for assessing impairment to ensure public and occupational safety. Traditional measurement techniques are subjective, time-consuming, and do not directly measure physical impairment. This study introduces objective metrics derived from eye-tracking analytics to address these limitations. We employed a head-mounted display to present 20 subjects with smooth pursuit performance, horizontal saccade, and simple reaction time tasks. Individual and group performance was compared before and after cannabis use. Results demonstrated significant changes in oculomotor control post-cannabis consumption, with smooth pursuit performance showing the most substantial signal. The objective eye-tracking data was used to develop supervised learning models, achieving a classification accuracy of 89% for distinguishing between sober and impaired states when normalized against baseline measures. Eye-tracking is the optimal candidate for a portable, rapid, and objective tool for assessing cannabis impairment, offering significant improvements over current subjective and indirect methods. Show Chinese abstract

Abstract: 持续增长的大麻合法化需要开发快速、客观的评估能力的方法，以确保公众和职业安全。 传统的测量技术是主观的、耗时的，并且不能直接测量身体能力的损害。 本研究引入了来自眼动分析的客观指标，以解决这些限制。 我们使用了头戴式显示器，向20名受试者展示了平滑追踪表现、水平扫视和简单反应时间任务。 比较了大麻使用前后的个人和群体表现。 结果表明，大麻消费后眼动控制有显著变化，其中平滑追踪表现显示出最显著的信号。 客观的眼动数据被用来开发监督学习模型，在将基准测量值进行归一化的情况下，实现了89%的分类准确率，用于区分清醒和受损状态。 眼动追踪是评估大麻能力的最佳候选便携式、快速和客观工具，相较于目前的主观和间接方法有显著改进。