Editorial: Human-centered solutions and synergies across robotic and digital systems for rehabilitation
Bibliographic record
Abstract
This is a provisional file, not the final typesetThe growing need for effective, personalized, clinically compliant, and engaging rehabilitation -based 31 on methodologies for the restoration of functions -can leverage the step-changes 32 offered by interaction technologies to obtain optimal results matching the initial requests of the users 33 (patients and clinicians). Human-Centered Design approaches may disclose the full potential of such 34 solutions, especially considering the impact of smart systems powered by robotic devices and digital 35 settings. In particular, virtual reality (VR) and augmented reality (AR) constitute a broad sub-class of 36 digital settings, often intertwined with serious games (including exergames devised to promote training 37 activities) and gamification (introducing game features in non-leisure solutions) for sustaining the 38 users' effort over time in repetitive exercises. Furthermore, they can be connected to smart mechatronic 39 systems (especially through their artificial intelligence -AI -features) for achieving higher versatility 40 and efficiency (making rehabilitation more sustainable for the individual and for the healthcare system 41 as a whole, as in telerehabilitation frameworks) (Adlakha, Chhabra, & Shukla, 2020;
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.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.033 | 0.030 |
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".