Multimodal data fusion for accurate psychological stress assessment using HRV and facial features
Bibliographic record
Abstract
In recent years, multimodal data analysis has gained widespread attention in psychological stress research. This study designs an experimental framework based on the Montreal Stress Model Theory, using mental arithmetic tasks as a stress induction method, to develop a stress assessment model based on physiological and behavioral features. The experiment is divided into three phases: no stress, moderate stress, and high stress. A comprehensive stress induction and analysis system is constructed using multimodal data collection techniques, including electrocardiogram (ECG) signals and facial expressions. In terms of data processing, the VGG16 deep learning model is used to extract facial video features, and a set of physiological features is constructed by calculating the time-domain and frequency-domain features of heart rate variability (HRV). A multimodal feature set containing both physiological and behavioral data is created. The TabNet model is employed for classification and identification of different stress states. Experimental results show that this method significantly improves the accuracy of stress state assessment, with the multi-parameter fusion model achieving an identification accuracy of 94.56%, providing crucial technical support for intelligent psychological stress assessment and personalized interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".