Arousal and Valence Estimation for Visual Non-Intrusive Stress Monitoring
Bibliographic record
Abstract
As the capabilities and usefulness of advanced Unmanned Aerial Vehicles (UAVs) increase, communication between the operator and these intelligent systems is becoming a very important factor for mission success. In this context, automatic stress detection is becoming a key research topic in emotion analysis. Stress can be estimated by means of an array of intrusive sensors or via the measurement of some biological markers (e.g. cortisol levels). However, these approaches are not appropriate in many cases of human-machine interactions. In this paper, we propose a deep learning-based psychological stress level estimation approach. The goal is to identify the region where the emotional state of the operator projects in the space defined by the latent dimensional emotions of arousal and valence. The stress region is well defined in this space according to prior works in psychology. The proposed predictive model first extracts and aligns the operator's face, then generates embeddings from a pre-trained face model. These embeddings are then used to train two different architectures, a hierarchical temporal CNN and a LSTM with an Attention Weighted Average layer. Since we deal with naturalistic behavior in a context of operator-machine interaction, the One-Minute Gradual-Emotion Behavior Challenge (OMG) dataset is used for the validation of continuously estimated arousal/valence levels.
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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) | 0.000 | 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".