Affective computing in serious games for physical rehabilitation: Scoping review (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Serious games have become an alternative support for traditional physical therapy. However, many of these games do not address the emotional needs of patients. People with disabilities often experience emotions such as sadness, frustration, and even anger, which can create a barrier to their rehabilitation treatment. </sec> <sec> <title>OBJECTIVE</title> This review aims to identify technologies and methods of affective computing applied in serious games for physical rehabilitation, establish a foundation for future research, and identify areas of opportunity for further exploration. </sec> <sec> <title>METHODS</title> A scoping review was conducted following PRISMA guidelines, using the databases PubMed, ScienceDirect, IEEE Xplore, ACM Digital Library, PEDro, Springer, and Google Scholar. </sec> <sec> <title>RESULTS</title> The initial search yielded 5,293 records, of which 9 articles met the inclusion criteria. Data were systematically extracted from these articles based on predefined research questions. Notably, engagement, tiredness, and pain were the most identified emotions, reported in 50% of the studies. Only three studies applied theoretical frameworks for emotion classification. Facial expression analysis and gesture recognition were the most frequently employed affective computing techniques, yet only two studies implemented adaptive gameplay based on emotional feedback. </sec> <sec> <title>CONCLUSIONS</title> This scoping review revealed that none of the studies validate the benefits of affective computing in the rehabilitation process, suggesting that future work should adopt more rigorous methodological designs. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".