Computationally tuned dual-layer lattice pads adapted to gait-induced pressure distribution
Bibliographic record
Abstract
Patients with Becker muscular dystrophy (BMD) often experience forefoot overload due to abnormal gait patterns, resulting in chronic pain, instability, and ulceration. Conventional orthotic devices frequently fail to accommodate the complex and individualized biomechanical needs of these patients. Here, we introduce a computational framework that integrates finite element analysis, machine learning, and Bayesian optimization to design dual-layer lattice pads adapted to gait-induced pressure distribution. A Gaussian Process Regression model accurately predicted structure–function relationships, enabling efficient multi-objective optimization within a high-dimensional design space. The optimized lattice configuration reduced peak plantar pressure by 51.36% during simulated gait, outperforming previous solutions. Unlike conventional uniform-stiffness insoles, the dual-layer architecture allows gradient mechanical tuning through parametric control, providing enhanced pressure offloading and structural adaptability. This work proposes a computationally scalable and theoretically generalizable framework for precision orthotic design and outlines a pathway for integrating computational biomechanics with AI-informed material engineering in future personalized rehabilitation technologies.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".