The initiation of the swing phase in human infant stepping: importance of hip position and leg loading
Bibliographic record
Abstract
Hip extension and low load in the extensor muscles are important sensory signals that allow a decerebrate or spinal cat to advance from the stance phase to the swing phase during walking. We tested whether the same sensory information controlled the phases of stepping in human infants. Twenty-two infants between the ages of 5 and 12 months were studied during supported stepping on a treadmill. Forces exerted by the lower limbs, surface electromyography (EMG) from muscles, and the right hip angle were recorded. The whole experimental session was videotaped. The hip position and the amount of load experienced by the right limb were manipulated during stepping by changing the position of the foot during the stance phase or by applying manual pressure on the pelvic crest. Disturbances with different combinations of hip position and load were used. The stance phase was prolonged and the swing phase delayed when the hip was flexed and the load on the limb was high. In contrast, stance phase was shortened and swing advanced when the hip was extended and the load was low. The results were remarkably similar to those in reduced preparations of the cat. They thus suggest that the behaviour of the brainstem and spinal circuitry for walking may be similar between human infants and cats. There was an inverse relationship between hip position and load at the time of swing initiation, indicating the two factors combine to regulate the transition.
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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".