Modeling and Simulation of a Lower Extremity Powered Exoskeleton
Bibliographic record
Abstract
Lower extremity powered exoskeletons (LEPEs) allow people with spinal cord injury (SCI) to stand and walk. However, the majority of LEPEs walk slowly and users can become fatigued from overuse of forearm crutches, suggesting LEPE design can be enhanced. Virtual prototyping is a cost-effective way of improving design; therefore, this research developed and validated two models that simulate walking with the Bionik Laboratories' ARKE exoskeleton attached to a human musculoskeletal model. The first model was driven by kinematic data from 30 able-bodied participants walking at realistic slow walking speeds (0.2-0.8 m/s) and accurately predicted ground reaction forces (GRF) for all speeds. The second model added upper limb crutches and was driven by 3-D-marker data from five SCI participants walking with ARKE. Vertical GRF had the strongest correlations (>0.90) and root-mean-square error (RMSE) and mediolateral center of pressure trajectory had the weakest (<0.35), for both models. Strong correlations and small RMSE between predicted and measured GRFs support the use of these models for optimizing LEPE joint mechanics and improving LEPE design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".