EMG-based estimation of muscular efforts exerted during human movements
Bibliographic record
Abstract
<titre>Résumé</titre>La quantification des efforts musculaires peut être d’une importance considérable dans les différents domaines de la biomécanique. Cependant, l’estimation des moments musculaires agoniste et antagoniste ou de la force développée par chaque muscle au cours d’un mouvement nécessite de résoudre le problème de la redondance musculaire. Cet article fait la synthèse des différentes étapes du développement d’une méthode « EMG-assistée » permettant de quantifier les moments et les tensions musculaires lors de contractions isométriques ou de tâches dynamiques, en présence ou non de fatigue. En associant de manière appropriée l’optimisation numérique et l’utilisation de données électromyographiques, la méthode proposée améliore la qualité de l’estimation de ces efforts musculaires. Cette approche pourra trouver de nombreuses applications, particulièrement en biomécanique du sport.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".