Simulation of Hip Abduction-Adduction Exoskeletons for Assisting Frontal-Plane Stability in Elderly Individuals
Bibliographic record
Abstract
Frontal-plane instability is positively correlated with higher incidence of severe injuries in elderly individuals. The effect of hip abduction-adduction assistance on stability in elderly individuals is not fully understood. This study investigates how the magnitude, timing, and location of hip abduction-adduction assistance affects the margin of stability. Methods: The OpenSim biomechanics software was used to generate simulations of eighteen elderly individuals while they stood with both feet on the floor and a lateral perturbation force of magnitude 5%, 10%, or 15% of bodyweight was applied to the pelvis. Contralateral, ipsilateral, or bilateral hip abduction-adduction assistance was applied following the perturbation. The change in margin of stability was used to predict the effectiveness of each assistance strategy and for comparison across subjects. Results: All assistance strategies improved the margin of stability; the greatest mean improvement was provided by the contralateral assistance strategy. For the 5%, 10%, and 15% bodyweight perturbations, contralateral assistance of 0.75 N$\cdot $m/kg improved margin of stability by$11.9~\pm ~1.41$mm,$11.9~\pm ~1.39$mm, and$12.0~\pm ~1.39$mm, respectively. Conclusion: The margin of stability can be improved by applying hip abduction-adduction assistance to the contralateral hip with fast actuation strategies. Significance: The results of this study can be used by exoskeleton designers to improve the stability of elderly individuals by adopting a hip abduction-adduction assistance strategy.
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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 | 0.000 |
| 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) | 0.002 | 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".