Efficient Deep Learning on Wearable Physiological Sensor Data for Pilot Flight Performance Analysis
Bibliographic record
Abstract
With the proliferation of wearable sensors for physiological and cognitive monitoring, a large amount of time series data needs to be processed and analyzed in a timely fashion. While deep learning has shown to be useful for the analysis, the majority of the deep learning methods are computing resource intensive. This paper demonstrates an efficient deep learning approach by adapting MINIROCKET to eye tracking and electrodermal activity data for flight performance assessment. The model was trained on 35 subjects using leave-one-subject-out cross validation and further evaluated on an independent data set of 8 subjects. We performed dimensionality reduction on each time series observation, reducing the size by 99.7% while still achieving averaged Area Under the Curve of 0.912 and average equal error rate of 0.181, thus enabling fast and accurate inference on edge devices. The approach presented here can be implemented in real-world cockpits for near instantaneous performance monitoring and could also be extended beyond this domain to other resource constrained time series applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".