In high-pressure environments where human individuals must simultaneously monitor multiple entities, communicate effectively, and maintain intense focus, the perception of time becomes a critical factor influencing performance and well-being. One indicator of well-being can be the person's subjective time perception. In our project $ChronoPilot$, we aim to develop a device that modulates human subjective time perception. In this study, we present a method to automatically assess the subjective time perception of air traffic controllers, a group often faced with demanding conditions, using their physiological data and eleven state-of-the-art machine learning classifiers. The physiological data consist of photoplethysmogram, electrodermal activity, and temperature data. We find that the support vector classifier works best with an accuracy of 79 % and electrodermal activity provides the most descriptive biomarker. These findings are an important step towards closing the feedback loop of our $ChronoPilot$-device to automatically modulate the user's subjective time perception. This technological advancement may promise improvements in task management, stress reduction, and overall productivity in high-stakes professions.

翻译：在人类个体必须同时监控多个实体、有效沟通并保持高度专注的高压环境中，时间感知成为影响绩效与身心健康的关键因素。个体的主观时间感知可作为其身心状态的一项指标。在我们的项目$ChronoPilot$中，我们旨在开发一种能够调节人类主观时间感知的设备。本研究提出一种方法，利用空中交通管制员（这一常面临严苛工作条件的群体）的生理数据及十一种先进机器学习分类器，对其主观时间感知进行自动评估。生理数据包括光电容积脉搏波、皮肤电活动及温度数据。我们发现支持向量分类器表现最佳，准确率达79%，且皮肤电活动提供了最具区分度的生物标志物。这些发现是完善$ChronoPilot$设备反馈闭环以实现用户主观时间感知自动调节的重要步骤。此项技术进步有望为高风险职业的任务管理、压力缓解及整体工作效率带来提升。