A Comparison of Control Activity and Heart Rate as Measures of Pilot Workload in a Helicopter Tracking Task
Bibliographic record
Abstract
Two qualified test pilots performed a target tracking flight task on a Bell 205 helicopter. Cooper-Harper handling quality ratings confirmed that pilot compensation was proportional to task difficulty. Pilot control activity was measured using the Dynamic Interface Modeling and Simulation System Product Metric (DIMSS-PM) to quantify the number and amplitude of control deflections during each trial. Inter-beat interval measures of heart rate and heart rate variability were also computed to evaluate the pilot's autonomic nervous system (ANS) response to task workload. The DIMSS-PM was positively correlated with task difficulty, as expected based on the dynamics of target motion for each difficulty level. By comparison, the mean and high-frequency (HF) variability of heart beat intervals were negatively correlated with task difficulty, suggesting an increase in ANS arousal with increased pilot workload. Pilot-specific differences were found in the time-dependent relationship between DIMSS-PM, mean heart beat interval, and HF variability, indicating that control activity and heart rate metrics provide asynchronous and complementary information about pilot workload during helicopter flight. NOTATION ANOVA Analysis of Variance ANS Autonomic Nervous System DIMMS-PM Dynamic Interface Modeling and Simulation System Product Metric ECG Electrocardiogram FBW Fly-By-Wire FRL Flight Research Laboratory HR Heart Rate HRV Heart Rate Variability HQR Handling Qualities Rating NRC National Research Council Canada RRI Inter-beat (R-R) Interval RMS Root Mean Square RMSSD RMS of Successive Differences in RRI SDNN Standard Deviation of RRI HF High Frequency (0.15 - 0.4 Hz) LF Low Frequency (0.04 - 0.15 Hz)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".