Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers
<|reference_start|>Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers: 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.<|reference_end|>
@article{aust2024automatic,
    title={Automatic Classification of Subjective Time Perception Using Multi-modal
  Physiological Data of Air Traffic Controllers},
    author={Till Aust, Eirini Balta, Argiro Vatakis, Heiko Hamann},
    journal={arXiv preprint arXiv:2404.15213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15213},
    primaryClass={cs.HC cs.LG}
}