An adaptive design approach for AIGC-based VR rehabilitation training systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the advancement of Industry 5.0 and the implementation of Human-Centric Smart Manufacturing, there is a growing demand for personalised and diverse services in intelligent rehabilitation. To address the limitations of traditional rehabilitation devices with programmed training models that fail to meet individual patient needs, this paper proposes a rehabilitation training system framework that integrates virtual reality (VR), artificial intelligence-generated content (AIGC), and embedded sensors, combining intelligent perception and personalised recommendation. The system uses VR to create immersive scenarios, AIGC to intelligently generate personalised training plans, and sensors to provide real-time feedback. Using smart gloves as an example, the system is evaluated by integrating VR task data, sensor data, and user subjective scale data. Results demonstrate that the framework significantly enhances rehabilitation outcomes and user acceptance. This study offers an innovative paradigm for intelligent rehabilitation device design and provides practical evidence for intelligent manufacturing and service innovation in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it