Standardizing the Development of Serious Games for Physical Rehabilitation: Conceptual Framework Proposal
Bibliographic record
Abstract
BACKGROUND: Serious games have been used as supportive therapy for traditional rehabilitation. However, most are designed without a systematic process to guide their development from the phases of requirement identification, planning, design, construction, and evaluation, which reflect the lack of adaptation of rehabilitation requirements and thus the patient's needs. OBJECTIVE: The aim of this study was to propose a conceptual framework with standardized elements for the development of information systems by using a flexible and an adaptable process centered on the patient's needs and focused on the creation of serious games for physical rehabilitation. METHODS: The conceptual framework is based on 3 fundamental concepts: (1) user-centered design, which is an iterative design process focused on users and their needs at each phase of the process, (2) generic structural activities of software engineering, which guides the independent development process regardless of the complexity or size of the problem, and (3) gamification elements, which allow the transformation of obstacles into positive and fun reinforcements, thereby encouraging patients in their rehabilitation process. RESULTS: We propose a conceptual framework to guide the development of serious games through a systematic process by using an iterative and incremental process applying the phases of context identification, user requirements, planning, design, construction of the interaction devices and video game, and evaluation. CONCLUSIONS: This proposed framework will provide developers of serious games a systematic process with standardized elements for the development of flexible and adaptable software with a high level of patient commitment, which will effectively contribute to their rehabilitation process.
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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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".