Feasibility of Low-Cost Virtual Reality Motor Amplification for Stroke Rehabilitation
Bibliographic record
Abstract
Abstract Purpose Virtual reality (VR) shows promise for supporting stroke rehabilitation and motor recovery. One design feature is motor amplification, a reinforcement-based approach with early evidence for reducing learned non-use among stroke survivors. However, its clinical use remains limited, with little understanding of its technical feasibility and user experience. To inform the ongoing development of VR rehabilitation systems, we assessed the technical feasibility and tolerability of automated, controller-free motor amplification in healthy young adults. Design Here we outline the initial development of the REVIVE system, a VR stroke rehabilitation system operating on the standalone Meta Quest line of Head Mounted Displays which are wireless, low cost, and require no external hardware. Hand tracking, voice recognition and an automated motor amplification algorithm enable accessible engagement for users. An animated coach in the virtual environment guides users through gamified exercises which simulate activities of daily living and functional movements. We tested the system with 60 healthy young adults, with a primary goal of validating the feasibility of the controller-free amplification feature on a low-cost headset. Findings Users reported minimal visually induced motion sickness even when experiencing the visuomotor perturbation generated by amplification, and more positive attitudes toward VR technology after the experience. Additionally, we provide a young healthy reference dataset for several REVIVE tasks to serve as a healthy baseline for future research with clinical populations. Value Our findings suggest that the addition of motor amplification to a consumer-grade system is technically feasible, has good user acceptance, and demonstrates short-term tolerability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".