Optimization of a Synergy-Driven Musculoskeletal Model to Estimate Muscle Excitations and Joint Moments of the Ankle Joint at Different Walking Speeds
Bibliographic record
Abstract
Control frameworks of powered prostheses should be fine-tuned to user-specific requirements as well as adaptable to different movement conditions, in order to be adopted as a beneficial solution. Otherwise, the lack of support from the prosthesis might lead to the adoption of inappropriate postures while walking and standing. The objective of the current study is to optimize a generic synergy model of muscle excitations to fit subject-specific torque at the ankle joint through a scaled musculoskeletal model across various speeds. The synergy optimization framework developed in this study was based on an interior point optimizer (IPOPT) combined with direct collocation. The framework was validated by comparing resultant moments from inverse dynamics with computed torques. Results show that the model is able to reproduce the ankle moment of six subjects at six different speeds (RMSE$0.1277\pm 0.02$). We propose the current solution as an accurate method of personalizing control framework for powered prostheses across different walking conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".