Is improving gait post-stroke at the tip of our fingers? the effects on enhanced sensory input (haptics and walking aids)
Bibliographic record
Abstract
While postural control during quiet stance can be enhanced by haptic input, the role of tactile cues on gait ability in healthy or neurological populations is not well understood. The aim of this study was to examine the effects of enhanced somatosensory input from (1) light fingertip touch and (2) cane use, on gait performance during level and slope walking in people post-stroke. Nine people post-stroke and 9 healthy individuals walked on a self-paced treadmill mounted on a motion platform while viewing a virtual scene. The experimental conditions were to walk level, up or down a 5° sloped surface: 1) without touch, 2) with light fingertip touch on a bar and 3) with an instrumented cane. Gait variability, expressed as the coefficient of variation of stride duration (CV), step width and gait speed were measured. Results reveal that light touch is an effective means of improving gait in people post-stroke. Light touch can be as effective compared to the cane even under the challenge of slope walking and may be more effective in downslope walking. Task specific (level, up and downslope) stabilizing strategies (use of either light touch or a cane) may offer specific gait improvements. These effects were not seen in the healthy controls.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".