Design Method of a Smart Rehabilitation Product Service System Based on Virtual Scenarios: A Case Study
Bibliographic record
Abstract
The development of artificial intelligence and virtual reality technology has enabled rehabilitation service systems based on virtual scenarios to provide patients with a multi-sensory simulation experience. However, the design methods of most rehabilitation service systems rarely consider the physician-manufacturer synergy in the patient rehabilitation process, as well as the problem of inaccurate quantitative evaluation of rehabilitation efficacy. Thus, this study proposes a design method for a smart rehabilitation product service system based on virtual scenarios. This method is important for upgrading the rehabilitation service system. First, the efficacy of rehabilitation for patients is quantitatively assessed using multimodal data. Then, an optimization mechanism for virtual training scenarios based on rehabilitation efficacy and a rehabilitation plan based on a knowledge graph are established. Finally, a design framework for a full-stage service system that meets user needs and enables physician-manufacturer collaboration is developed by adopting a "cloud-end-human" architecture. This study uses virtual driving for autistic children as a case study to validate the proposed framework and method. Experimental results show that the service system based on the proposed methods can construct an optimal virtual driving system and its rehabilitation program based on the evaluation results of patients' rehabilitation efficacy at the current stage. It also provides guidance for improving rehabilitation efficacy in the subsequent stages of rehabilitation services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".