A simple, clinically applicable motor learning protocol to increase push-off during gait: A proof-of-concept
Bibliographic record
Abstract
OBJECTIVE: Task-specific training is often used in functional rehabilitation for its potential to improve performance at locomotor tasks in neurological populations. As push-off impairment are often seen with these patients, this functional approach shows potential to retrain gait overground to normalize the gait pattern and retrain the ability to improve gait speed. The main objective of this project was to validate, in healthy participants, a simple, low-cost push-off retraining protocol based on task-specific training that could be implemented during overground walking in the clinic. METHODS: 30 healthy participants walked in an 80-meter long corridor before, during, and after the application of an elastic resistance to the right ankle. Elastic tubing attached to the front of a modified ankle-foot orthosis delivered the resistance during push-off. Relative ankle joint angular displacements were recorded bilaterally and continuously during each walking condition. RESULTS: On the resisted side, participants presented aftereffects (increased peak plantarflexion angle from 13.4±4.2° to 20.0±6.4°, p<0.0001 and increased peak plantarflexion angular velocity from 145.8±22.7°/s to 174.4±37.4°/s, p<0.0001). On the non-resisted side, aftereffects were much smaller than on the resisted side suggesting that the motor learning process was mainly specific to the trained leg. CONCLUSION: This study shows the feasibility of modifying push-off kinematics using an elastic resistance applied at the ankle while walking overground. This approach represents an interesting venue for future gait rehabilitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".