Modelling the relationship between the objective measures of car sickness
Bibliographic record
Abstract
Car sickness is anticipated to occur more frequently in self-driving vehicles because of their design, especially the electronics and seating arrangements optimized for work and entertainment. Therefore, mitigating motion sickness is a crucial research area that is essential to the effective use of electronics in autonomous vehicles and, ultimately, their broad adoption. An investigation using machine learning techniques in combination with physiological measures (electrocardiogram, electrodermal activity and head movement) was done to detect and predict the severity of car sickness. A total of 40 adults (20 male and 20 female) were exposed to two 20-minute rides on a motion-base simulator, one while reading and one while performing no task. Car sickness incidence and severity were subjectively measured during the conditions using the Fast Motion Sickness Scale (FMS) questionnaire every two minutes and the Simulator Sickness Questionnaire (SSQ) at the beginning, midpoint and end of the experiment. Car sickness symptoms were successfully elicited in 31 participants (77.5%) while avoiding simulator sickness. Head movement had the strongest relationship with car sickness, and there was a moderate correlation between heart rate and skin conductance, and with a subset of participants, heart rate had a moderate correlation with car sickness. A classification score of 77% distinguishing between motion-sick and non-motion-sick participants was found using the random forest model. Overall, the findings suggest that physiological measures alone cannot be relied upon to reliably detect or predict the onset or severity of car sickness in real-time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".