A Novel Elastic Model for Exoskeleton-User Coupling Interfaces
Bibliographic record
Abstract
Current research on physical human-robot interactions (pHRI) in wearable assistive robots, such as lower-limb exoskeletons, primarily focuses on improving net force estimates at each interface to improve robot controller performance. Consequently, estimating force distribution along physical interfaces of wearable robots, crucial for user safety and comfort, has been largely overlooked. We propose a novel computational model that uses interface geometry and strapping tension as inputs, and predicts the static pressure field generated during the user donning process by treating the supporting surface as an elastic foundation. Accuracy of the proposed computational method was validated by comparing the estimated static pressure field of a commercially available interface to experimental data. While measured pressure magnitudes were significantly lower than model prediction, likely due to a combination of assumptions and limitations associated with model design, similar loading patterns were observed. Identifying regions of high pressure from simulation and similar patterns allow for reliable scaling to reduce inaccuracies, and may be used to inform design. Further refinements of the proposed model will provide a valuable tool for developing more comfortable and safer interfaces for wearable robots.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".