Error Augmentation in Immersive Virtual Reality for Bimanual Upper-Limb Rehabilitation in Individuals With and Without Hemiplegic Cerebral Palsy
Bibliographic record
Abstract
With more readily available commercial immersive virtual reality (VR) technologies, the potential of new feedback strategies as tools to facilitate motor rehabilitation should be investigated. Augmented feedback or error augmentation (EA) can easily be shown in a virtual environment. Here, visual EA provided via immersive VR was tested for its effectiveness to improve bimanual symmetry in a reaching task. A single-session crossover design was used to test two training cases, with or without EA. With EA, the distance between hands in the forward direction was augmented. Participants were recruited from typically developing (TD) populations (n = 12, ages 13-21) and performed in an adapted environment with an initial asymmetry between limbs. Also, five participants with hemiplegic cerebral palsy (CP) (ages 14-21, MACS I-III) completed the study. Among TD participants, a significantly larger change in symmetry in the adapted environment was shown after EA than training without EA (F (1, 10) = 9.64, p = 0.01). Each participant in the CP group also improved more after EA training (8.8-103.7)%, such that they achieved lower symmetry error after training with EA. As participants in both groups adapted more symmetrically with EA, beneficial changes from this training method could be evaluated in future studies for longer-term functional changes.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".