A Fusion Model for Cross-Subject Stress Level Detection Based on Transfer Learning
Bibliographic record
Abstract
Stress is a psychological condition that affects daily life, and chronic stress can result in cardiovascular disease and reduced productivity. Mental stress can be induced when difficult and time-limited tasks are assigned. Several groups have studied the relationship between physiologic signals and a subject's stress level. Through machine learning and signal processing, stress level can be automatically inferred from raw physiologic signals. As each person can have a specific physiologic reaction pattern to stress, it becomes problematic for a classifier to work well on a new subject. In this study, transfer learning is used to solve the problem of inter-subject variability. Methods are developed here to classify five levels of stress based on physiologic signals comprising photoplethysmogram (PPG), galvanic skin response (GSR), abdominal respiration, and thoracic respiration. Domain adaptation methods based on information-theoretical learning and transfer component analysis (TCA) are shown to reduce inter-subject variability of both GSR and respiratory signals. A fusion model was also designed to combine classification scores from each signal to reduce the effect of low-quality recording. The proposed method is shown to increase accuracy from 68.79% to 76.70% and Intraclass Correlation Coefficient (ICC) from 83.82% to 96.55%.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".