Use of the Fast Orthogonal Search Method to Estimate Optimal Joint Angle For Upper Limb Hill-Muscle Models
Bibliographic record
Abstract
An important aspect of accurate representation of human movement is the ability to account for differences between individuals. The following paper proposes a methodology using Hill-based candidate functions in the fast orthogonal search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force-prediction framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. Surface electromyography data from three muscles of the upper arm (biceps brachii, brachioradialis, and triceps brachii) were recorded from ten subjects, as they performed isometric contractions at varying elbow joint angles. Estimated muscle activation level and joint angle were utilized as inputs to the FOS model. Subject-specific estimates of optimal joint angles for the three muscles were determined via frequency analysis of the selected FOS candidate functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".