Assessment of Motion Sickness Associated with Motor Amplification in a Low-Cost Virtual Reality Rehabilitation System
Bibliographic record
Abstract
Effective rehabilitation is critical for individuals with motor deficits, such as following a stroke or brain injury. Although high doses of intensive motor rehabilitation can provide maximal benefit, several barriers can prevent attaining necessary therapy levels, including cost, physical access, clinical resource availability, and patient disengagement. Virtual Reality (VR) offers a potential solution to overcome these challenges and enable home-based, self-directed therapy. Here we present custom software that runs on the low-cost Meta Quest 2 headset to deliver immersive rehabilitation exercises. The system uses movement amplification to virtually reduce motor deficits, enabling successful task completion even in individuals with severe disabilities. This approach leverages reinforcement learning to maintain patient engagement during rehabilitation. The system capitalizes on recent advances in VR technology, including inside-out hand tracking, voice recognition, and a responsive virtual coach, to create a more accessible environment for users with no prior VR experience. We present the custom virtual rehabilitation implementation and test whether motor amplification leads to an increase in visually induced motion sickness experienced by individuals without disabilities. We find that motor amplification does not elevate reported motion sickness levels, suggesting that low-cost VR systems may enable increased access to individualized and guided motor rehabilitation.
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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 | 0.000 | 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 | 0.001 |
| 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".