An in-flight multimodal data collection method for assessing pilot cognitive states and performance in general aviation
Bibliographic record
Abstract
Human factors are central to aviation safety, with pilot cognitive states such as workload, stress, and situation awareness playing important roles in flight performance and safety. Although flight simulators are widely used for training and scientific research, they often lack the ecological validity needed to replicate pilot cognitive states from real flights. To address these limitations, a new in-flight data collection methodology for general aviation using a Cessna 172 aircraft, which is one of the most widely used aircraft for pilot training, is presented. The dataset combines: • Human data from wearable physiological sensors (electroencephalography, electrocardiography, electrodermal activity, and body temperature) and eye-tracking glasses. • Flight data from ADS-B flight recorder. • Pilot's self-reported cognitive states and flight performance rate by instructor. The paper describes the sensor setup, flight task design, and data synchronization procedures. Potential analyses using statistical and machine learning methods are discussed to classify cognitive states and demonstrate the dataset's value. This methodology supports human factors research and has practical value for applications in pilot training, performance evaluation, and aviation safety management. The method was applied in a field study with 25 participants, from which 20 complete multimodal datasets were retained after data cleaning. After collecting additional data, the resulting dataset will support further research on pilot performance and behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".