Model and Characterization of a Passive Biomimetic Ankle for Lower Extremity Powered Exoskeleton
Bibliographic record
Abstract
Lower extremity powered exoskeletons (LEPE) allow people with spinal cord injury (SCI) to perform activities of daily living, such as standing, walking, or stair and ramp ascent/descent. However, current LEPE walk slowly and require extensive use of forearm crutches to maintain user stability. Consequently, this limits LEPE time of use and overall system performance. While the biological ankle is known to be critical for energy efficiency, speed, and stability in able-bodied walking, current LEPE do not include biomimetic ankle designs and thus limit device performance. The objective of this thesis is to determine biomimetic ankle mechanics for a LEPE, thereby defining ankle design requirements that could reduce crutch loads and thus extend LEPE use. Virtual prototyping techniques were used to achieve this objective. Two 3D models of a real LEPE (ARKE, Bionik Laboratories) attached to a human musculoskeletal model were developed and validated. The first model (biomimetic model) was driven by 3D marker kinematics from 30 able-bodied participants walking at four realistically slow LEPE walking speeds. The second model (SCI model) was driven by 3D marker kinematics from five SCI participants walking in the ARKE LEPE with instrumented forearm crutches. Once the models were validated by comparing predicted to measured ground reaction forces (GRF) and centre of pressure (COP) trajectories, biomimetic LEPE ankle design requirements were determined. Ankle range of motion, quasi-stiffness, work, peak moment, and peak power were compared between human and human+ARKE models, across four gait phases and four slow walking speeds. The major findings were: the human+ARKE model had significantly different quasi-stiffness values across all four gait phases; quasi-stiffness increased with increasing speed; the human+ARKE model’s ankle always absorbed net-work, even at the fastest walking speed; quadratic regression was significantly more accurate than linear regression for modelling ankle quasi-stiffness. These results suggested that passive variable stiffness ankles incorporating quadratic elastic spring elements could achieve biomimetic ankle functions and thus potentially increase LEPE user walking speed, stability, and reduce overuse of crutches.
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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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".