Gait Training after Stroke on a Self-Paced Treadmill with and without Virtual Environment Scenarios: A Proof-of-Principle Study
Bibliographic record
Abstract
Purpose: The purpose of this proof-of-principle study was to show that virtual reality (VR) technology could be coupled with a self-paced treadmill to further improve walking competency in individuals with chronic stroke. Method: A 62-year-old man with a chronic right hemispheric stroke participated in a treadmill walking programme involving first a control (CTL) protocol, then VR training. In CTL training, he walked without time constraints while viewing still pictures and reacting to treadmill movements similar to those that he would have experienced later in VR training. In VR training, he experienced treadmill movements programmed to simulate changes encountered in five virtual environments rear-projected onto a large screen. Training difficulty in nine sessions over 3 weeks was increased by varying the time constraints, terrain surface changes, and obstacles to avoid. Effects on walking competency were assessed using clinical measures (5 m walk test, 6 min walk test, Berg Balance Scale, Activities-specific Balance Confidence scale) and questionnaires (Assessment of Life Habits Scale and personal appraisal). Results: CTL and VR training resulted in a similar progression through the training sessions of total time walked on the treadmill. The VR training led to an additional increase in speed as measured by walking 5 metres as fast as possible and distance walked in 6 minutes, as well as improved balance self-efficacy and anticipatory locomotor adjustments. As reported by the participant, these improved outcomes transferred to real-life situations. Conclusions: Despite the limited potential for functional recovery from chronic stroke, an individual can achieve improvements in mobility and self-efficacy after participating in VR-coupled treadmill training, compared with treadmill training with the same intensity and surface perturbations but without VR immersion. A larger scale, randomized controlled trial is warranted to determine the efficacy of VR-coupled treadmill training for mobility intervention post-stroke.
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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".