Timing-Specific Transfer of Adapted Muscle Activity After Walking in an Elastic Force Field
Bibliographic record
Abstract
Human locomotion results from interactions between feedforward (central commands from voluntary and automatic drive) and feedback (peripheral commands from sensory inputs) mechanisms. Recent studies have shown that locomotion can be adapted when an external force is applied to the lower limb. To better understand the neural control of this adaptation, the present study investigated gait modifications resulting from exposure to a position-dependent force field. Ten subjects walked on a treadmill before, during, and after exposure to a force field generated by elastic tubing that pulled the foot forward and up during swing. Lower limb kinematics and electromyographic (EMG) activity were recorded during each walking period. During force field exposure, peak foot velocity was initially increased by 38%. As subjects adapted, peak foot velocity gradually returned to baseline in ≤125 strides. In the adapted state, hamstring EMG activity started earlier (16% before toe off) and remained elevated throughout swing. After force field exposure, foot velocity was initially reduced by 22% and returned to baseline in 9–51 strides. Aftereffects in hamstring EMGs consisted of increased activity around toe off. Contrary to the adapted state, this increase was not maintained during the rest of swing. Together, these results suggest that while the neural control of human locomotion can adapt to force field exposure, the mechanisms underlying this adaptation may vary according to the timing in the gait cycle. Adapted hamstring EMG activity may rely more on feedforward mechanisms around toe off and more on feedback mechanisms during the rest of swing.
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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".