Multi-level Stress Assessment From Electrocardiogram Data in a Virtual Reality Environment Using Contrastive Self Supervision
Bibliographic record
Abstract
Electrocardiogram (ECG) data is an ideal option to assess stress in Virtual Reality (VR) applications due to its non-invasive nature and high correlation to changes in stress levels. Yet, most studies on stress assessment only collect binary measurements. Developing a multi-level assessment is necessary for a biofeedback-based application. Existing studies annotate and classify a single experience (e.g. watching a VR video) to a single stress level, which again prevents design of dynamic experiences where real-time in-game stress assessment can be utilized. Furthermore, existing works heavily rely on supervision information to train stress assessment models. However, obtaining supervised labels for training data is an expensive and labor intensive task that is not scalable on large datasets. Given the advancements in data driven technologies, supervised learning prevents us from leveraging unlabeled data excessively available nowadays. In this thesis, we report our findings on a new study on VR multi-level stress assessment. ECG data was collected from 12 participants experiencing a VR roller coaster. The VR experience was then manually labeled in 10-second segments to three stress levels. We then propose a contrastive self-supervised learning approach for stress assessment that is superior to non-contrastive self-supervised approaches. Experimental results show that when compared to non-contrastive self-supervised learning, we obtained a 9% increase in accuracy on the WESAD dataset and a 3.7% increase on our collected data at the Ryerson Multimedia Lab.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".