Preliminary Exploration on Timing the VR to AR Transition in Upper Limb Prosthesis Training: A Simulation Study
Bibliographic record
Abstract
Determining when to move upper-limb prosthesis users from risk-free Virtual Reality (VR) drills to real-world or Augmented Reality (AR) tasks is often guesswork. We propose a data-driven rule that identifies the VR practice duration after which further workload reductions become negligible, signaling the optimal switch to AR. Modeling workload as a negatively accelerated learning curve with four interpretable parameters, a 1,000-run Monte Carlo simulation across clinically plausible ranges found optimal VR exposure clustered between 2-6 hours (median ≈ 4.8h). Continuing beyond this point cut workload by <2 NASA-TLX points but extended VR time by up to 50%, while switching earlier left users with ~15% higher workload entering AR. This patient-specific benchmark operationalizes therapists’ "train-until-plateau" intuition, supporting efficient, transparent scheduling and real-time adaptation, though empirical validation under non-ideal learning patterns is still needed.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".