Strategies for Dynamic Stability During Locomotion on a Slippery Surface: Effects of Prior Experience and Knowledge
Bibliographic record
Abstract
Falls due to slips are prevalent in everyday life. The purpose of this study was to determine the reactive recovery responses used to maintain dynamic stability during an unexpected slip, establish the time course of response adaptation to repeated slip perturbations, and distinguish the proactive strategies for negotiating a slippery surface. Twelve young adults participated in the study in which a slip was generated following foot contact on a set of steel free-wheeling rollers. Surface electromyographic (EMG) data were collected from rectus femoris, biceps femoris, tibialis anterior, and the medial head of gastrocnemius on the perturbed limb. Whole body kinematics were recorded using an optical imaging system: from this the center of mass, foot angle, and medial-lateral stability margins were determined. In addition, braking/loading and accelerating/unloading impulses while in contact with the rollers and the rate of loading the rollers were determined from ground reaction forces. Results demonstrate that the reactive recovery response to the first slip consisted of a rapid onset of a flexor synergy (146-199 ms), a large arm elevation strategy, and a modified swing limb trajectory. With repeated exposure to the slip perturbation, the CNS rapidly adapts within one slip trial through global changes. These changes include the attenuation of muscle response magnitude, reduced braking impulse, landing more flat-footed, and elevating the center of mass. Individuals implement a "surfing strategy" while on the rollers when knowledge of the surface condition was available before hand. Furthermore, knowledge of a slip results in a reduced braking impulse and rate of loading, a shift in medial-lateral center of mass closer to the support limb at foot contact on the rollers and a more flat foot landing. In conclusion, prior experience with the perturbations allows subsequent modification and knowledge of the surface condition results in proactive adjustments to safely traverse the slippery surface.
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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 | 0.004 |
| 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) | 0.002 | 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".