STUDY ON DEEP LEARNING MODELS FOR THE CLASSIFICATION OF VR SICKNESS LEVELS
Bibliographic record
Abstract
Virtual Reality (VR) sickness is often accompanied by symptoms such as nausea and dizziness, and a prominent theory explaining this phenomenon is the sensory conflict theory. Recently, studies have used Deep Learning to classify VR sickness levels; however, there is a paucity of research on Deep Learning models that utilize both visual information and motion data based on sensory conflict theory. In this paper, the authors propose a parallel merging of a Deep Learning model (4bay) to classify the level of VR sickness by utilizing the user's motion data (HMD, controller data) and visual data (rendered image, depth image) based on sensory conflict theory. The proposed model consists of a visual processing module, a motion processing module, and an FC-based VR sickness level classification module. The performance of the proposed model was compared with that of the developed models at the time of design. As a result of the comparison, it was confirmed that the proposed model performed better than the single model and the merged (2bay) model in classifying the user's VR sickness level.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".