An Approach for Automatic Adaptation of Serious Games Applied to Virtual Motor Rehabilitation
Bibliographic record
Abstract
Virtual rehabilitation has seen the use of game design principles to create serious games and exergames aimed at further involving the patient in therapy to increase adherence and compliance. The addition of game design elements to the therapeutic context can keep patients engaged throughout the exercises, which are characterized as repetitive and monotonous. Studies involving virtual rehabilitation with the use of serious games have obtained promising results, but there are still challenges regarding the possibility of automatic adaptation based on metrics associated with the limitations and needs of each individual. This paper proposes an approach to automatic adaptation to serious games applied to virtual motor rehabilitation. A game was developed as a proof of concept and experiments were conducted with physiotherapists and typical users to evaluate the feasibility of applying it in real therapy sessions. The results indicate the preference of physiotherapists for games with automatic adaptation, which demonstrates positive attitudes toward integrating this technology into rehabilitation practices. Usability feedback from healthcare professionals and typical users suggested ease of interaction and highlighted areas for improvement, including the potential of virtual rehabilitation games to optimize therapy outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".