Cybersickness Marker Prediction Using a Biosensors-Instrumented VR Headset: A Pilot Study
Bibliographic record
Abstract
Cybersickness is a major concern in virtual reality (VR) experiences. Understanding and quantifying this phenomenon is essential to improve user comfort and enhance the overall VR experience. In this study, we explore the use of a custom-built por table VR headset equipped with multimodal biosensors to develop markers of cybersickness. The headset can record/stream electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and head movement data in real-time. A pilot experiment with eight participants was conducted where, at the end of a VR session at the gamers’ homes, participants rated their cybersickness symptoms across eight dimensions: headache, eyestrain, increased salivation and sweat, nausea, fullness of the head, dizziness with eyes open, and vertigo. From the collected biosignals, a series of features were extracted, ranked and explored as uni-or multi-modal markers for the different cybersickness ratings. Overall, it was found that EEG features were useful as correlates of vertigo, dizziness, and increased sweat, while head movements for nausea and headache. When multimodal markers were explored, improved cybersickness characterization was achieved for seven of the eight ratings. It is hoped that these findings will help researchers to better understand cybersickness and offers ways to improve the VR experience for users.
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".