Effect of Handrail Height and Age on Trunk and Shoulder Kinematics Following Perturbation-Evoked Grasping Reactions During Gait
Bibliographic record
Abstract
OBJECTIVE: To characterize the effect of handrail height and age on trunk and shoulder kinematics, and concomitant handrail forces, on balance recovery reactions during gait. BACKGROUND: Falls are the leading cause of unintentional injury in adults in North America. Handrails can significantly enhance balance recovery and help individuals to avoid falls, provided that their design allows users across the lifespan to reach and grasp the rail after balance loss, and control their trunk by applying hand-contact forces to the rail. However, the effect of handrail height and age on trunk and shoulder kinematics when recovering from perturbations during gait is unknown. METHOD: Fourteen younger and 13 older adults experienced balance loss (sudden platform translations) while walking beside a height-adjustable handrail. Handrail height was varied from 30 to 44 inches (76 to 112 cm). Trunk and shoulder kinematics were measured via 3D motion capture; applied handrail forces were collected from load cells mounted to the rail. RESULTS: As handrail height increased (up to 42 inches/107 cm), peak trunk angular displacement and velocity generally decreased, while shoulder elevation angles during reaching and peak handrail forces did not differ significantly between 36 and 42 inches (91 and 107 cm). Age was associated with reduced peak trunk angular displacements, but did not affect applied handrail forces. CONCLUSION: Higher handrails (up to 42 inches) may be advantageous for trunk control when recovering from destabilizations during gait. APPLICATION: Our results can inform building codes, workplace safety standards, and accessibility standards, for safer handrail design.
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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 | 0.002 |
| 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".