Locomotor rehabilitation in a complex virtual environment
Bibliographic record
Abstract
Virtual reality (VR) technology offers a new and safe way to increase practice time and provide the varied environments and constraints needed to optimize locomotor training. Our specific objectives are (1) to create a virtual environment (VE) coupled with a self-paced treadmill for locomotor training; (2) to compare temporal and distance measurements of gait during treadmill walking while looking at different scenarios of VE; and (3) to develop a protocol optimized for the training of locomotor disorders after stroke. A motorized treadmill was mounted on a six-degree-of-freedom motion platform. VEs were created using commercial software (SoftImage) and projected on a large screen, while system control was administered through the CAREN software (Motek BV). The instantaneous treadmill speed and scene progression were servo-controlled. Preliminary results show that healthy subjects are able to have full control of their own walking speed both on the treadmill and within the virtual scene, while experiencing a strong sense of presence. A street crossing training protocol has been developed for locomotor training. It is expected that locomotor training with increasingly complex VEs will allow persons with stroke to increase progressively their locomotor capacity, as required and entrained by the VE.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".