Genetic Algorithms Based Approach for Designing Spring Brake Orthosis – Part I: Spring Parameters
Bibliographic record
Abstract
Spring brake orthosis (SBO) concentrates purely on the knee to generate the swing phase of the paraplegic gait with the required hip flexion occurring passively as a consequence of the ipsilateral knee flexion, generated by releasing the torsion spring mounted at the knee joint. Electrical stimulation then drives the knee back to full extension, as well as restores the spring potential energy. In this paper, genetic algorithm (GA) and its variant multi-objective GA (MOGA) is used to perform the search operation for the ‘best’ spring parameters for the SBO spring mounted on an average sized subject simulated in the sagittal plane. Conventional torsion spring is tested against constant torque type spring in terms of swing duration as, based on first principles, it is hypothesized that constant torque spring would be able to produce slower SBO swing phase as might be preferred in assisted paraplegic gait. In line with the hypothesis, it is found that it is not possible to delay the occurrence of the flexion peak of the SBO swing phase further than its occurrence in the natural gait. The use of conventional torsion spring causes the swing knee flexion peak to appear rather faster than that of the natural gait, resulting in a potentially faster swing phase and hence gait cycle. The constant torque type spring on the other hand is able to stretch duration of the swing phase to some extent, rendering it the preferable spring type in SBO.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".