Enhancing Fatigue Detection through Heterogeneous Multi-Source Data Integration and Cross-Domain Modality Imputation
arXiv:2507.16859v5 Announce Type: replace-cross Abstract: Fatigue detection for human operators is important in safety-related applications such as aviation, mining, and long-haul transport. Reliable estimation of operator fatigue can support timely warnings, adaptive task scheduling, takeover reminders, and other safety-management decisions in human-machine systems. However, the effectiveness of these functions depends on whether fatigue-related signals can be reliably captured in the deployment environment. While many studies have shown the value of high-fidelity sensors in controlled labora
The paper leverages advances in AI and multi-modal data integration, which are currently maturing quickly, to address a perpetual safety challenge in high-risk human-machine systems.
Reliable fatigue detection in critical human-operator roles can significantly enhance safety and efficiency in industries vital to global infrastructure, offering both humanitarian and economic benefits.
The ability to accurately detect human operator fatigue through diverse sensor data, even in challenging deployment environments, moves from theoretical possibility to practical application, potentially impacting industries from aviation to logistics.
- · Aviation Industry
- · Mining Companies
- · Logistics/Transport Sector
- · AI/Sensor Tech Providers
- · Companies with high incident rates due to human error
- · Insurance providers (initially, due to adaptions to new risk profiles)
Improved safety records and reduced incidents in fatigue-sensitive industries.
Increased regulatory requirements and mandates for fatigue monitoring systems in critical operator roles.
The development of truly adaptive human-machine interfaces that dynamically adjust tasks based on real-time operator cognitive states, blurring lines of autonomous and human control.
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Read at arXiv cs.AI