TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102575 - 102575

Published: May 20, 2024

Language: Английский

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Tracking the Unseen and Unaware: Deciphering Controllers’ Detection Failures to Warnings Through Eye-Tracking Metrics

International Journal of Human-Computer Interaction, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: Jan. 13, 2025

Language: Английский

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A robust operators’ cognitive workload recognition method based on denoising masked autoencoder

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 301, P. 112370 - 112370

Published: Aug. 14, 2024

Language: Английский

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An integrated framework for eye tracking-assisted task capability recognition of air traffic controllers with machine learning

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102784 - 102784

Published: Aug. 24, 2024

Language: Английский

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Cognitive Workload Estimation in Conditionally Automated Vehicles Using Transformer Networks Based on Physiological Signals

Transportation Research Record Journal of the Transportation Research Board, Journal Year: 2024, Volume and Issue: unknown

Published: June 10, 2024

Though driving automation promises to improve safety, drivers are still required be ready retake control in conditionally automated vehicles, which defined by the Society of Automotive Engineers (SAE) as SAE L3 vehicles. Thus, drivers’ states can affect safety Previous research found that a high cognitive load may impair takeover performance. it is necessary estimate However, existing driver estimation algorithms mostly focus on vehicles with lower level (e.g., L0), not relevant when estimating given responsibilities different, and several commonly used measures performance) unavailable continuously controlling vehicle. Further, previous rarely considered temporal information input features. we proposed deep-learning algorithm integrated multiple physiological features (i.e., electrocardiogram, electrodermal activity, respiration) correlation data using transformer-encoder-based network. The performance our was compared baseline models an open set. Results showed outperformed achieved accuracy 94.4% within-subject partition (proportionally splitting from same subject into training testing sets) 89% across-subjects (dividing sets based individual subjects).

Language: Английский

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A human-centric model for task demand assessment based on unsupervised learning-assisted eye movement measure

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103259 - 103259

Published: March 14, 2025

Language: Английский

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Neural correlates of augmented reality safety warnings: EEG analysis of situational awareness and cognitive performance in roadway work zones

Safety Science, Journal Year: 2025, Volume and Issue: 185, P. 106802 - 106802

Published: Feb. 5, 2025

Language: Английский

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Dual-perspective safety driver secondary task detection method based on swin-transformer and cross-attention

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103320 - 103320

Published: April 6, 2025

Language: Английский

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Human operators’ cognitive workload recognition with a dual attention-enabled multimodal fusion framework

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127418 - 127418

Published: April 1, 2025

Language: Английский

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Machine Learning Based on Eye Movement Indicators to Detect Fatigue in Coal Mine Monitoring Dispatcher

Iranian Journal of Science and Technology Transactions of Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 26, 2025

Language: Английский

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