Methodology to Customize Maximal Isometric Forces for Hill-Type Muscle Models
Bibliographic record
Abstract
One approach to increasing the confidence of muscle force estimation via musculoskeletal models is to minimize the root mean square error (RMSE) between joint torques estimated from electromyographic-driven musculoskeletal models and those computed using inverse dynamics. We propose a method that reduces RMSE by selecting subsets of combinations of maximal voluntary isometric contraction (MVIC) trials that minimize RMSE. Twelve participants performed 3 elbow MVIC in flexion and in extension. An upper-limb electromyographic-driven musculoskeletal model was created to optimize maximum muscle stress and estimate the maximal isometric force of the biceps brachii, brachialis, brachioradialis, and triceps brachii. Maximal isometric forces were computed from all possible combinations of flexion-extension trials. The combinations producing the smallest RMSE significantly reduced the normalized RMSE to 7.4% compared with the combination containing all trials (9.0%). Maximal isometric forces ranged between 114-806 N, 64-409 N, 236-1511 N, and 556-3434 N for the brachii, brachialis, brachioradialis, and triceps brachii, respectively. These large variations suggest that customization is required to reduce the difference between models and actual participants' maximal isometric force. While the smallest previously reported RMSE was 10.3%, the proposed method reduced the RMSE to 7.4%, which may increase the confidence of muscle force estimation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".