Building Trustworthy Cognitive Monitoring for Safety-Critical Human Tasks: A Phased Methodological Approach
June 27, 2025 Β· Declared Dead Β· π SpaceCHI
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Maciej Grzeszczuk, Grzegorz Pochwatko, Barbara Karpowicz, StanisΕaw KnapiΕski, WiesΕaw KopeΔ
arXiv ID
2506.22066
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
SpaceCHI
Last Checked
4 months ago
Abstract
Operators performing high-stakes, safety-critical tasks - such as air traffic controllers, surgeons, or mission control personnel - must maintain exceptional cognitive performance under variable and often stressful conditions. This paper presents a phased methodological approach to building cognitive monitoring systems for such environments. By integrating insights from human factors research, simulation-based training, sensor technologies, and fundamental psychological principles, the proposed framework supports real-time performance assessment with minimum intrusion. The approach begins with simplified simulations and evolves towards operational contexts. Key challenges addressed include variability in workload, the effects of fatigue and stress, thus the need for adaptive monitoring for early warning support mechanisms. The methodology aims to improve situational awareness, reduce human error, and support decision-making without undermining operator autonomy. Ultimately, the work contributes to the development of resilient and transparent systems in domains where human performance is critical to safety.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted