Form Switching During Human Locomotion: Traversing Wedges in a Single Step
Bibliographic record
Abstract
We examined the neural control strategies used to accommodate discrete alterations in walking surface inclination. Normal subjects were tested walking on a level surface and on different wedges (10 degrees, 15 degrees, 20 degrees, and 30 degrees ) presented in the context of level walking. On a given trial, a subject walked on a level surface in approach to a wedge, took a single step on the wedge, and continued walking on an elevated level surface beyond the wedge. As wedge inclination increased, subjects linearly increased peak joint angles. Changes in timing of peak joint angles and electromyograms were not linear. Subjects used two distinct temporal strategies, or forms, to traverse the wedges. One form was used for walking on a level surface and on the 10 degrees wedge, another form for walking on the 20 degrees and 30 degrees wedges. In the level/10 degrees form, peak hip flexion occurred well before heel strike (HS) and peak dorsiflexion occurred in late stance. In the 20 degrees /30 degrees form, peak hip flexion was delayed by 12% of the stride cycle and peak dorsiflexion was reached 12% earlier. For the level/10 degrees form, onsets of the rectus femoris, gluteus maximus, and vastus lateralis muscles were well before HS and offset of the anterior tibialis was at HS. For the 20 degrees /30 degrees form, onsets of the rectus femoris, gluteus maximus, and vastus lateralis and offset of the anterior tibialis were all delayed by 12% of the stride cycle. Muscles shifted as a group, rather than individually, between the forms. Subjects traversing a 15 degrees wedge switched back and forth between the two forms in consecutive trials, suggesting the presence of a transition zone. Differences between the forms can be explained by the differing biomechanical constraints imposed by the wedges. Steeper wedges necessitate changes in limb orientation to accommodate the surface, altering limb orientation with respect to gravity and making it necessary to pull the body forward over the foot. The use of different forms of behavior is a common theme in neural control and represents an efficient means of coordinating and adapting movement to meet changing environmental demands. The forms of locomotion reported here are likely used on a regular basis in real-world settings.
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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".