The Switching of Trailing Limb Anticipatory Locomotor Adjustments is Uninfluenced by what the Leading Limb Does, but General Time Constraints Remain
Bibliographic record
Abstract
Research shows a blend of bilateral influence and independence between leading and trailing limbs during obstacle avoidance. Recent research also shows time constraints in switching leading limb strategies. The present study aimed to understand the ability to switch anticipatory locomotor adjustments (ALAs) in the trailing limb. Ten healthy young adults (24 ± 3 years) were immersed in a virtual environment requiring them to plan and step over an obstacle that, for the trailing limb, could change to a platform, requiring a switch in locomotor strategies to become a leading limb to step onto a new surface. Such perturbations were provoked at either late planning or early execution of the initial trailing limb obstacle avoidance. Sagittal plane trailing limb kinematics, joint kinetics and energetics were measured along with electromyographic activity of key lower limb muscles. Repeated measures ANOVAs compared dependent variables across conditions. To adjust to the new environment, knee flexor power around toe-off decreased (p < 0.001) and hip flexor power increased (p < 0.001) for late planning phase perturbations, while there was only an increase in mid-swing hip flexor power (p < 0.05) during perturbations at execution. Findings showed no influence of the leading limb function on the ability to switch trailing limb ALAs during late planning. However, the trailing limb was also constrained for modifying ALAs once execution began, but on-going limb control strategies could be exploited in a reactive mode.
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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) | 0.002 | 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".