Software Engineering Frameworks Used for Serious Games Development in Physical Rehabilitation: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Serious games are a support in the rehabilitation process for treating people with physical disabilities. However, many of these serious games are not adapted to the patient's needs because they are not developed with a software engineering framework with a set of activities, actions, and tasks that must be executed when creating a software product. Better serious games for rehabilitation will be developed if the patient and therapist requirements are identified, the development is planned, and system improvements and feedback are involved. The goal is that the serious game must offer a more attractive environment, while maintaining patient interest in the rehabilitation process. OBJECTIVE: This paper submits the results of a systematic review of serious games in physical rehabilitation identifying the benefits of using a software engineering framework. METHODS: A systematic research was conducted using PubMed, PEDro (Physiotherapy Evidence Database), IEEE Xplore, ScienceDirect, ACM Digital Library, Mary Ann Liebert, Taylor & Francis Online, Wiley Online Library, and Springer databases. The initial search resulted in 701 papers. After assessing the results according to the inclusion criteria, 83 papers were selected for this study. RESULTS: From the 83 papers reviewed, 8 used a software engineering framework for its development. Most of them focused their efforts on 1 or more aspects, such as data acquisition and processing, game levels, motivation, therapist supervision. CONCLUSIONS: This systematic review proves that most of the serious games do not use a software engineering framework for their development. As a result, development systems overlook several aspects and do not have a standardized process, eventually omitting important implementation aspects, which impact the patient's recovery time.
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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 | 0.033 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".