A Longitudinal Study on the Effects of Circadian Fatigue on Sound Source Identification and Localization using a Heads-Up Display

要旨

Circadian fatigue, largely caused by sleep deprivation, significantly diminishes alertness and situational awareness. This issue becomes critical in environments where auditory awareness—such as responding to verbal instructions or localizing alarms—is essential for performance and safety. While head-mounted displays have demonstrated potential in enhancing situational awareness through visual cues, their effectiveness in supporting sound localization under the influence of circadian fatigue remains under-explored. This study addresses this knowledge gap through a longitudinal study (N=19) conducted over 2–4 months, tracking participants’ fatigue levels through daily assessments. Participants were called in to perform non-line-of-sight sound source identification and localization tasks in a virtual environment under high- and low-fatigue conditions, both with and without head-up display assistance. The results show task-dependent effects of circadian fatigue. Unexpectedly, reaction times were shorter across all tasks under high-fatigue conditions. Yet, in sound localization, where precision is key, the HUD offered the greatest performance enhancement by reducing pointing error. The results suggest the auditory channel is a robust means of enhancing situational awareness and providing support for incorporating spatial audio cues and HUD as standard features in augmented reality platforms for fatigue-prone scenarios.

著者
Alexander G. Minton
University of Technology Sydney, Sydney, New South Wales, Australia
Howe Yuan. Zhu
University of Sydney, Camperdown, New South Wales, Australia
Hsiang-Ting Chen
University of Adelaide, Adelaide, South Australia, Australia
Yu-Kai Wang
University of Technology Sydney, Sydney, NSW, Australia
Zhuoli Zhuang
University of Technology Sydney, Sydney, NSW, Australia
Gina Notaro
Lockheed Martin, Cherry Hill, New Jersey, United States
Raquel Galvan
Lockheed Martin, Arlington, Virginia, United States
James Allen
Lockheed Martin, Cherry Hill, New Jersey, United States
Matthias D. Ziegler
Lockheed Martin, Arlington, Virginia, United States
Chin-Teng Lin
Australian AI Institute, School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia
DOI

10.1145/3706598.3713402

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713402

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Auditory UI

G402
7 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
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