Increased affect-arousal in VR can be detected from faster body motion with increased heart rate
Bibliographic record
Abstract
We instrumented an immersive VR platform with physiological (heart rate and electrodermal activity) sensors to investigate the use of movement data and physiological data to automatically detect changes in affect (emotional state). 12 users were asked to complete four blocks of tasks requiring them to hit moving targets while standing and moving about. One of the four blocks (in counterbalanced order) was designed to be stressful (S), while the other blocks were designed to be calm (C). The motions required of the users were the same in both conditions; only the visual and audio feedback were different across the S and C conditions. Users' self-scored arousal in the S condition was significantly higher. We analyzed the recorded motions by segmenting out 2747 "fast motions", i.e., intervals of time where the sum of the speed of the hands was above a threshold. A simple machine learning algorithm (a decision tree) could learn to classify these fast motions as either calm or stressed, with ≈80% accuracy, using only two features: the maximum speed achieved during the motion, and the heart rate at the moment of maximum speed, where both features were normalized. If only the maximum speed feature is used (i.e., with no physiological data), ≈70% accuracy is achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".