A virtual reality-based training system for error-augmented treatment in patients with stroke
Bibliographic record
Abstract
Stroke is a leading cause of long-term sensorimotor deficits in upper limb function and current upper limb interventions have limited effectiveness. Joint-level augmentation treatment, grounded in referent control theory, prescribes insertion of error at the joint level for inducing a dynamic re-mapping of muscle-leve control mechanisms. We hypothesize that this will lead to an increase in the control range of the joint and consequently to improved performance of voluntary motion. In the current presentation we describe a system harnessing virtual reality developed for upper-limb training based on joint level error augmentation. The system comprises three components, a passive arm rest supporting the arm against gravity, a Kinect motion tracking system, and a virtual-reality training environment. The visualization of the entire arm is a critical system component which should invoke a high degree of presence. For the method to be effective, the participant should accept the visualized arm position as representing his/her actual arm location, despite conflicting input from his/her proprioception. A pilot test is currently under way for assessing the method's effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".