Adaptive Immersive Virtual Reality Exergame for Post-Stroke Rehabilitation: Design and Implementation
Bibliographic record
Abstract
This study introduces an immersive virtual reality (IVR) exergame with real-time kinematic adaptation, aimed at upper limb rehabilitation in individuals with post-stroke sequelae. The relevance of this work lies in its contribution to the development of personalized therapeutic tools capable of continuously adjusting exercise intensity to maintain controlled physical effort and enhance treatment adherence. The system dynamically modulates task difficulty based on motion analysis, using dimensionless jerk as a key kinematic feature to estimate movement smoothness and predict perceived fatigue. A quasi-experimental pilot study was conducted with two independent groups: a control group (n = 10) using the non-adaptive version, and an experimental group (n = 10) interacting with the adaptive exergame. Data were collected through the Borg Rating of Perceived Fatigue and the Virtual Reality Neuroscience Questionnaire (VRNQ), both administered after a single session. Statistical analysis revealed significant differences between groups (p < 0.05), indicating greater physical activation in the adaptive group without reaching excessive fatigue levels, as well as a more immersive and satisfactory user experience. These findings suggest that IVR systems with real-time kinematic adaptation represent a promising strategy for optimizing motor rehabilitation in post-stroke populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".