Design and Sensor Based Evaluation of Custom-Fit Coupling Interfaces for Lower Limb Exoskeletons: A Pilot Study
Bibliographic record
Abstract
The coupling interface on wearable lower limb exoskeletons influence physical human-robot interactions (pHRI) that occur during assistance in gait and posture tasks. Despite their increasing popularity, risks to the user (i.e., skin, musculoskeletal injuries) from undesirable interactions at the interface are present. Current research examining coupling interactions, focuses on characterizing simple pressure measures (i.e., mean) independent of interface features. Consequently, design guidelines of ideal coupling interface interactions do not yet exist due to a lack of common framework and metrics tied to interface features for analysis. Other wearable devices (e.g. prostheses), use custom fit interfaces as a baseline for interface design. In this study we compare custom made single-user and generic multi-user interfaces using established and novel pressure-related measures. 5 minute overground gait trials were conducted with pressure sensors placed at the hip and thigh interfaces. Pilot study results showed increased supporting contact area at the measured surfaces and a reduction in relative movement during use of custom-fit interfaces. Average and peak pressure varied between sites, with custom thigh interfaces exhibiting notable improved performance. Pressure measures reported highlight the benefit of custom interfaces, and the value of additional pressure analysis metrics in differentiating interface designs and evaluating relative performance. We propose that custom fit interfaces can provide a common baseline for evaluating coupling interface pHRI, supported by novel methods of pressure analysis to tie pHRI improvements to specific interface design characteristics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".