Virtual Reality applied to Post-Stroke Rehabilitation: design and development of the NeuroRehab VR Software
Bibliographic record
Abstract
ABSTRACT Objective To describe the design and development of NeuroRehab VR, a fully immersive, specific and gamified virtual reality (VR) software aimed at improving the quality of life and reducing disability in post-stroke patients. Methods A public-private collaborative research project was carried out between 2022 and 2024 by a multidisciplinary and multicenter team comprising neurologists, rehabilitation specialists, physiotherapists, exercise and sport sciences professionals and members of the company Dynamics VR Rehab, including engineers, developers, computer programmers, game designers, and digital artists. The project was structured into three phases: preproduction, production, and postproduction, with periodic focus group meetings and testing sessions with patients in the subacute phase of stroke held every one to two months. Results In the Preproduction phase, the multidisciplinary team discussed the initial concepts and, using the SCRUM methodology together with feedback from pilot patients, developed the software design. In this process, three thematic environments (i.e., home, nature, and science fiction) were established, along with five activity types targeting upper limb rehabilitation: fine motor skills, gross motor skills, balance, rhythmic movements, and movement speed. The software incorporated fully immersive VR, advanced hand tracking technology, and adaptive gamification elements. During the Production phase, these components were implemented and consolidated into a functional prototype. Finally, in the post-production phase, several adjustments were made after identifying minor issues, with the aim of improving activity responsiveness and refining the user experience for both patients and clinicians. Conclusion NeuroRehab VR represents a promising tool to be integrated into post-stroke rehabilitation programs and is being tested though a clinical trial. Moreover, this public-private, multidisciplinary, and multicenter collaboration model constitutes an effective framework for the design and development of technologically driven solutions applicable to clinical rehabilitation settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".