Exploratory Analysis of Game Metrics of a Multi-Session Study of a Virtual Reality Exergame for Stroke Rehabilitation.
Bibliographic record
Abstract
Introduction: Virtual Reality (VR) is a key tool in post-stroke rehabilitation, enhancing engagement and enjoyment. It benefits upper limb recovery by allowing personalized treatments based on precise performance metrics, thereby accelerating progress and making the experience more interactive. Objective: This study explores performance data from 10 stroke participants over 12 sessions using the custom VR game VR, aiming to show how game metrics, such as scores, provide insights into therapeutic progress. Methodology: Ten individuals over 50 years old recovering from a stroke participated in 12 sessions of VR, using three game mechanics: Boxes, Tejos, and Branches. The Difficulty Adjusted Performance Index (DAPI) was calculated to assess performance adjusted for task difficulty. The Wilcoxon signed-rank test compared performance metrics between the first and last sessions. Results: Significant performance improvements were observed in all game mechanics. The number of destroyed boxes, tejos, and branches cut was significantly higher in the last session compared to the first (boxes: z=-1.962, p=0.050; tejos: z=-1.992, p=0.046; branches: z=-2.397, p=0.017). DAPI analysis showed notable performance improvement, highlighting the effectiveness of VR. Conclusion: This study confirms the effectiveness of VR in post-stroke rehabilitation, demonstrating significant improvements in game performance. The use of DAPI and statistical analysis underscores progress in participant performance, showcasing VR and game metrics as valuable tools in post-stroke recovery by integrating challenge and motivation into therapy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".