A Treadmill and Motion Coupled Virtual Reality System for Gait Training Post-Stroke
Bibliographic record
Abstract
A virtual reality (VR)-based locomotor training system has been developed for gait rehabilitation post-stroke. The system consists of a self-paced treadmill mounted onto a 6-degrees-of-freedom motion platform. Virtual environments (VEs) that are synchronized with the speed of the treadmill and the motions of the platform are rear-projected onto a screen in front of the walking subject. A feasibility study was conducted to test the capability of two stroke patients and one healthy control to be trained with the system. Three VE scenarios (corridor walking, street crossing, and park stroll) were woven into a gait-training program that provided three levels of complexity (walking speed, slopes, collision avoidances), progression criteria (number of successful trials) and knowledge of results. Results show that, with practice, patients can effectively increase their gait speed as demanded by the task and adapt their gait with respect to the change in physical terrain. However, successful completion of tasks requiring adaptation to increasing demands related to speed and physical terrains does not necessarily predict the patient's ability to anticipate and avoid collision with obstacles during walking. This feasibility study demonstrates that persons with stroke are able to adapt to this novel VR system and be immersed in the VEs for gait training.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".