Control of Dynamic Stability During Gait Termination on a Slippery Surface
Bibliographic record
Abstract
There are three common ways by which to successfully terminate gait: decreased acceleration of whole-body center of mass (COM) through a flexor synergy in the trail leg, increased deceleration of whole-body COM through an extensor synergy in the front limb, and an energy/momentum transfer to dissipate any remaining momentum if the first two strategies are unsuccessful. Healthy individuals were asked to stop on a slippery surface while we examined their unexpected response to the slippery surface. Kinetic data from the forceplates revealed lower braking forces in the slip trials compared with normal gait-termination trials. Subjects were unable to control their center of pressure (COP) to manipulate the COM as revealed by increased deviations and maximum absolute ranges of COP movement. Subject COM deviated farther in both horizontal planes and lowered further during the slip compared with normal gait-termination trials. Arm movements were effective in dissipating forward COM movement. In addition, there likely was a transfer of forward to lateral momentum to stop forward progression. All recorded muscle activity in the lower limbs and back increased during the slip to provide support to the lower limbs and correct upright balance. The trailing limb shortened its final step to provide support to the lowering COM. The balance-correction response seen here resembles previous reactions to perturbations during locomotion suggesting there is a generalized strategy employed by the nervous system to correct for disturbances and maintain balance.
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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".