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

TL;DR

This paper presents a machine learning approach to automatically assess subjective time perception in air traffic controllers using physiological data, aiming to develop a device that can modulate perceived time to improve performance and well-being.

Contribution

It introduces a novel method combining physiological signals and machine learning classifiers to evaluate subjective time perception in high-pressure environments.

Findings

Support vector classifier achieved 79% accuracy.

Electrodermal activity was the most informative biomarker.

Method advances the development of a time perception modulation device.

Abstract

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.