The effect of light touch on balance control during overground walking in healthy young adults
Bibliographic record
Abstract
Balance control is essential for safe walking. Adding haptic input through light touch may improve walking balance; however, evidence is limited. This research investigated the effect of added haptic input through light touch in healthy young adults during challenging walking conditions. Sixteen individuals walked normally, in tandem, and on a compliant, low-lying balance beam with and without light touch on a railing. Three-dimensional kinematic data were captured to compute stride velocity (m/s), relative time spent in double support (%DS), a medial-lateral margin of stability (MOS ML ) and its variance (MOS ML CV), as well as a symmetry index (SI) for the MOS ML . Muscle activity was evaluated by integrating electromyography signals for the soleus, tibialis anterior, and gluteus medius muscles bilaterally. Adding haptic input decreased stride velocity, increased the %DS, had no effect on the MOS ML magnitude, decreased the MOS ML CV, had no effect on the SI, and increased activity of most muscles examined during normal walking. During tandem walking, stride velocity and the MOS ML CV decreased, while %DS, MOS ML magnitude, SI, and muscle activity did not change with light touch. When walking on a low-lying, compliant balance beam, light touch had no effect on walking velocity, MOS ML magnitude, or muscle activity; however, the %DS increased and the MOS ML CV and SI decreased when lightly touching a railing while walking on the balance beam. The decreases in the MOS ML CV with light touch across all walking conditions suggest that adding haptic input through light touch on a railing may improve balance control during walking through reduced variability.
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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) | 0.001 | 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".