A Discrete Non-Linear Series Elastic Actuator for Active Ankle-Foot Orthoses
Bibliographic record
Abstract
This letter outlines the modelling, design, and experimental validation of a novel power-efficient actuator for an active ankle-foot orthosis (AAFO). The actuator is based on a new principle of discrete non-linear stiffness. Two or more linear springs are discretely compressed at specified displacement intervals to reduce the peak mechanical power required to actuate the AAFO. The actuator uses a crank-rocker configuration. The connecting link is comprised of the discrete non-linear technique, a DC motor powers the crank, and the rocker is connected directly to the ankle joint. Multi-objective optimization is carried out to select the link lengths and spring stiffnesses to reduce input power and size. The actuator design weighs 460 g with bounding box dimensions of 103x45x94 mm. The newly proposed discrete non-linear stiffness configuration reduces the peak mechanical input power by 77.2% with respect to nominal biological ankle joint power. A prototype was developed and tested using static loading and human walking trials to verify the actuator models and simulations. The experimental results confirm the validity of the models by comparing the actual and theoretical ankle and motor torque values in the presence of discrete variable stiffness.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".