Detecting and predicting visually induced motion sickness with physiological measures in combination with machine learning techniques
Bibliographic record
Abstract
Visually induced motion sickness (VIMS) is a common sensation when using visual displays such as smartphones or Virtual Reality. In the present study, we investigated whether Machine Learning (ML) techniques in combination with physiological measures (ECG, EDA, EGG, respiration, body and skin temperature, and body movements) could be used to detect and predict the severity of VIMS in real-time, minute-by-minute. A total of 43 healthy younger adults (25 female) were exposed to a 15-minute VIMS-inducing video. VIMS severity was subjectively measured during the video using the Fast Motion Sickness Scale (FMS) as well as before and after the video using the Simulator Sickness Questionnaire (SSQ). Thirty-one participants (72%) experienced VIMS in the present study. Results showed that changes in facial skin temperature and body movement had the strongest relationship with VIMS. On a minute-by-minute basis, ML models revealed a medium correlation between the physiological measures and the FMS scores. An acceptable classification score distinguishing between sick and non-sick participants was found. Our findings suggest that physiological measures may be useful for measuring VIMS, but they are not a reliable standalone method to detect or predict VIMS severity in real-time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".