Design and Evaluation of a Quasi-Passive Variable Stiffness Prosthesis for Walking Speed Adaptation in People With Transtibial Amputation
Bibliographic record
Abstract
The biological ankle joint adjusts stiffness to adapt to changing walking speed, terrain, and load carriage. The most commonly used passive transtibial prostheses are unable to adjust device stiffness and therefore do not maximize potential energy storage and peak prosthesis power across speeds. We present a quasi-passive variable stiffness ankle–foot prosthesis with discrete stiffness adjustment from 352 to 479 Nm/radian, corresponding to the range of biological ankle quasi-stiffness exhibited during level ground walking at speeds from 0.75 to 1.5 m/s for a 77 kg person. We implement a novel parallel leaf spring mechanism that utilizes custom solenoid-driven linear actuators to constrain sliding of parallel leaf springs relative to a mechanical ground in order to control bending stiffness. The prosthesis is lower in mass than all existing variable stiffness prostheses, with a mass of 945 g. We present initial results from a pilot study with one participant with unilateral transtibial amputation, demonstrating an increase in range of motion, peak prosthesis power, and energy storage and return, and a decrease in contralateral knee external adduction moment across a range of walking speeds. This variable stiffness ankle–foot prosthesis demonstrates the potential to improve biomechanics of walking through the design of a low-mass, quasi-passive prosthesis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".