Applications of Musculoskeletal Modelling and Simulation for Lower-Limb Prosthesis Design Optimization
Bibliographic record
Abstract
Lower-limb amputees can suffer from preventable pain and bone disorders attributable to suboptimal prosthesis design. Predictive modelling and simulation of human walking using conventional biomechanical gait models offer an alternative to intuition-based prosthesis design, providing insight into the biomechanics underlying pathological gait. Musculoskeletal models additionally enable understanding of prosthesis contributions to the human musculoskeletal system, and both prosthesis and individual muscle contributions to body support and propulsion during gait. Based on this review, forward dynamic simulation of amputee musculoskeletal models have been used to perform prosthesis design optimization using optimal control and reflex-based control. Musculoskeletal model complexity and assumptions inhibit fully predictive musculoskeletal modelling in its current state, hindering computational prosthesis design optimization. Future recommendations include validating musculoskeletal models and resultant optimized prosthesis designs, developing less computationally-expensive predictive musculoskeletal modelling methods, and developing more efficient patient-specific musculoskeletal model generation methods to enable personalized prosthesis optimization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".