ArticleFrontiers in neuroscience2024
Pilot turning behavior cognitive load analysis in simulated flight.
Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Non-Invasive Physiological Metrics for Cognitive Load Assessment in Training and Operational Contexts: Signal Processing, Evidence, and Feasibility.Sensors (Basel, Switzerland) · 2026Review
- Spectral dynamic causal modeling of effective connectivity across multiple brain networks in pilot trainees.iScience · 2026Article
- Multimodal workload assessment during simulator-based instrument flight training.Frontiers in psychology · 2026Article
- Human limits in next-generation fighter aviation: psychophysiological stressors, readiness, health, performance, and countermeasures.Frontiers in physiology · 2026Review
- Workload analysis of pilot steep turn maneuvers using SR20 aircraft and EEG data.Frontiers in neuroscience · 2026Article
- Variations in mood states among Brazilian air force pilots: a crossover analysis between operational and aerobatic flight missions.Frontiers in physiology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To identify the cognitive load of different turning tasks in simulated flight, a flight experiment was designed based on real "preliminary screening" training modules for pilots. Methods: Heart Rate Variability (HRV) and flight data were collected during the experiments using a flight simulator and a heart rate sensor bracelet. The turning behaviors in flight were classified into climbing turns, descending turns, and level flight turns. A recognition model for the cognitive load associated with these turning behaviors was developed using machine learning and deep learning algorithms. Results: pnni_20, range_nni, rmssd, sdsd, nni_20, sd1, triangular_index indicators are negatively correlated with different turning load. The LSTM-Attention model excelled in recognizing turning tasks with varying cognitive load, achieving an F1 score of 0.9491. Conclusion: Specific HRV characteristics can be used to analyze cognitive load in different turn-ing tasks, and the LSTM-Attention model can provide references for future studies on the selection characteristics of pilot cognitive load, and offer guidance for pilot training, thus having significant implications for pilot training and flight safety.
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