Adapting Locomotion to Different Surface Compliances: Neuromuscular Responses and Changes in Movement Dynamics
Bibliographic record
Abstract
Knowledge of how the nervous system deals with surfaces with different physical properties such as compliance that challenge balance during locomotion is of importance as we are constantly faced with these situations every day. The purpose of this study was to examine the control of center of mass (COM) and lower limb dynamics and recovery response modulation of muscle activity during locomotion across an unexpected compliant surface and in particular, scaling behavior across different levels of compliance. Eight young adults walked along a walkway and stepped on an unexpected compliant surface in the middle of the travel path. There were three different levels of surface compliance, and participants experienced either no compliant surface or one of the three compliant surfaces during each trial that were presented in a blocked or random fashion. Whole body kinematics were collected along with surface electromyography (EMG) of selected bilateral lower limb and trunk muscles. The recovery response to the first compliant-surface trial demonstrated muscle onset latencies between 97 and 175 ms, and activity was modulated while on the compliant surface. Vertical COM trajectory was not preserved after contact with the compliant surface: peak vertical COM, while on the compliant surface was lower than when on stable ground. Perturbed-limb knee flexion after toe-off increased with increased surface compliance, which enabled toe clearance with the ground to be similar to control trials. The results suggest that stepping off of a compliant surface is actively modulated by the CNS and is geared toward maintaining dynamic stability.
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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".