# Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers

arXiv: https://arxiv.org/abs/2404.15213 · HTML: https://mlsanity.com/paper/2404.15213/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health](https://mlsanity.com/paper/2105.02808/) (2021)

## Built on by (most cited first, 2 of 2)

- [Predicting change in time production -- A machine learning approach to time perception](https://mlsanity.com/paper/2412.12781/) (2024)
- [Classifying Subjective Time Perception in a Multi-robot Control Scenario Using Eye-tracking Information](https://mlsanity.com/paper/2504.06442/) (2025)

## Abstract (arXiv)

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.