Maintaining stable transfemoral amputee gait on level, sloped and simulated uneven conditions in a virtual environment
Bibliographic record
Abstract
PURPOSE: Describe and quantify how people with transfemoral amputations (TFA) maintain stable gait over a variety of surfaces; including, downhill and uphill, top and bottom-cross-slopes, medial-lateral translations, rolling hills and simulated rocky surfaces. METHODS: Ten TFA and ten matched people without amputations (NA) walked in a virtual environment with level, sloped and simulated uneven surfaces on a self-paced treadmill. Stability was quantified using medial-lateral margin of stability (ML-MoS), step parameters, and gait variability (standard deviations for speed, temporal-spatial parameters, foot clearance and root-mean-square of medial-lateral trunk acceleration). RESULTS AND CONCLUSIONS: TFA and NA adapted to non-level conditions by changing their walking speed, step width, and foot clearance. Variability for most parameters increased across conditions, compared to level. TFA walked slower than NA with shorter, wider and longer duration steps (most differences related to speed). ML-MoS did not change compared to level; however, ML-MoS was greater on the prosthetic side than both intact side and NA limbs. Foot clearance and root-mean-square of medial-lateral trunk acceleration were greater on the prosthetic side than the intact side and NA limbs. This research provides a comprehensive analysis of the different adaptations made by people without amputations compared to people with transfemoral amputations over non-level conditions and establishes significant differences between slopes and simulated uneven surfaces for TFA. Implications for Rehabilitation Transfemoral amputation and no amputation groups adapted walking biomechanics when traversing non-level surfaces. Greatest temporal-spatial gait adaptations were walking speed, step width and foot clearance. Gait parameter variability typically increased from the level condition in both groups. Transfemoral amputation group walked slower than no amputation group with shorter, wider steps and longer duration steps. This was related to speed. Transfemoral amputation group had more trunk motion variability on the prosthetic side than no amputation group; could be related to prosthetic fit.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".