Maximal isometric force exertion predicted by the force feasible set formalism: application to handbraking
Bibliographic record
Abstract
The aim of this study was to test the capacity of the force feasible set formalism to predict maximal force exertion during isometric handbraking. Maximal force exertion and upper-limb posture were measured with a force sensor embedded in a handbrake and an optoelectronic system, respectively. Eleven subjects participated in the experiment which consisted of exerting the maximal force in isometric conditions considering five hand brake positions relative to the seat H-point. Then, maximal force was predicted by the force feasible set obtained from an upper-limb musculoskeletal model. The root-mean-square (RMS) of the angle between measured and predicted forces was 8.4° while the RMS error (RMSE) for amplitude prediction was 95.4 N. However, predicted, and measured force amplitudes were highly correlated (r = 0.88, p < 0.05, slope = 0.97, intercept = 73.3N) attesting the capacity of the model to predict force exertion according to the subject’s posture. The implications in the framework of ergonomics are then discussed. Practitioner summary: Maximal force exertion is of paramount importance in digital human modelling. We used the force feasible set formalism to predict maximal force exertion during handbraking from posture and anthropometric data. The predicted and measured force orientation showed a RMS of 8.4° while amplitude presented a RMSE of 95.4 N with a strong correlation (r = 0.88, p < 0.05, slope 0.97, intercept 77.3 N).
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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".