Design and Preliminary Evaluation of a Kinematically-Adaptive Immersive Virtual Reality Exergame for Post-Stroke Rehabilitation
Bibliographic record
Abstract
Introduction: Upper limb rehabilitation after a stroke faces challenges such as low motivation and adherence to traditional therapies. This study presents an Immersive Virtual Reality (IVR) exergame incorporating a kinematically adaptive difficulty system that dynamically modifies exercise complexity in real time based on movement data collected by VR equipment. Objective: To evaluate the effectiveness of an adaptive system based on dimensionless jerk to optimize therapeutic effort in post-stroke participants. Methodology: A usability study was conducted with 20 participants divided into a control group (non-adaptive version) and an experimental group (adaptive version). The Borg Fatigue Scale and the Virtual Reality Neuroscience Questionnaire (VRNQ) were used to measure engagement, perceived fatigue, and user experience. Results: The adaptive system improved participants’ engagement and therapeutic outcomes and therapeutic outcomes. The experimental group reported perceived physical effort levels closer to the ideal therapeutic range defined in the literature. Additionally, this group achieved higher evaluations in user experience and immersion. Conclusions: The exergame proved to be an effective and personalized tool for post-stroke rehabilitation. While areas for improvement were identified, such as responsiveness in advanced stages, this system offers a dynamic and motivating approach to optimizing recovery processes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".