A Mechanical Descriptor of Instability in Human Locomotion: Experimental Findings in Control Subjects and People with Transfemoral Amputation
Bibliographic record
Abstract
While multiple criteria to quantify gait instability exist, some limitations hinder their computation during realistic walking conditions. A descriptor, computed as the distance between the center of mass of the body and the minimal moment axis ( d C O M − Δ ) , has been proposed recently. This present study aims at characterizing the behavior of the mentioned descriptor in a population at a higher risk of falls. Five individuals with transfemoral amputation and 14 healthy individuals were involved in an experiment composed of motion capture and force plates acquisition during overground walking at a self-selected speed. For both groups of participants, the profile of d C O M − Δ was analyzed and descriptive parameters were calculated. The plot of d C O M − Δ was different between groups and different relative to the leading limb considered (prosthetic or contralateral). All descriptive parameters calculated, except one, were statistically different between groups. As a conclusion, amputees seem to be able to limit the average of d C O M − Δ in spite of a different evolution pattern. This is consistent with the ability of the subjects to maintain their dynamic balance. However, the extracted parameters showed the significant asymmetry of the gait profile between prosthetic and contralateral stances and highlighted the potential sources of imbalance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".