Design and Evaluation of Virtual Reality–Based Therapy Games with Dual Focus on Therapeutic Relevance and User Experience for Children with Cerebral Palsy
Bibliographic record
Abstract
OBJECTIVE: Virtual reality (VR)-based therapy for motor rehabilitation of children with cerebral palsy (CP) is growing in prevalence. Although mainstream active videogames typically offer children an appealing user experience, they are not designed for therapeutic relevance. Conversely, rehabilitation-specific games often struggle to provide an immersive experience that sustains interest. This study aims to design and evaluate two VR-based therapy games for upper and lower limb rehabilitation and to evaluate their efficacy with dual focus on therapeutic relevance and user experience. MATERIALS AND METHODS: Three occupational therapists, three physiotherapists, and eight children (8-12 years old), with CP Level I-III on the Gross Motor Function Classification System, evaluated two games for the Microsoft(®) (Redmond, WA) Kinect™ for Windows and completed the System Usability Scale (SUS), Physical Activity Enjoyment Scale (PACES), and custom feedback questionnaires. RESULTS: Children and therapists unanimously agreed on the enjoyment and therapeutic value of the games. Median scores on the PACES were high (6.24±0.95 on the 7-point scale). Therapists considered the system to be of average usability (50th percentile on the SUS). The most prevalent usability issue was detection errors distinguishing the child's movements from the supporting therapist's. The ability to adjust difficulty settings and to focus on targeted goals (e.g., elbow/shoulder extension, weight shifting) was highly valued by therapists. CONCLUSIONS: Engaging both therapists and children in a user-centered design approach enabled the development of two VR-based therapy games for upper and lower limb rehabilitation that are dually (a) engaging to the child and (b) therapeutically relevant.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".