Muscle Torque Generator Model For A Two Degree-of-Freedom Shoulder Joint
Notice bibliographique
Résumé
Muscle Torque Generators (MTGs) have been developed as an alternative to muscle-force models, reducing the complexity of muscle-force models to a single torque at the joint. Previous studies have been conducted to determine functions to scale joint torque based on position and velocity-dependent properties. However, current MTGs can only be applied to single Degree of Freedom (DOF) joints, leading to complications in modeling joints such as the shoulder, which has 3 DOF. Therefore, this project aimed to develop, for the first time, an MTG model that accounts for the coupling between 2 DOF at the shoulder joint, with shoulder plane of elevation and shoulder elevation being the DOF of interest. The 2 DOF MTG form was based on previous research for a single DOF MTG. Three different 2 DOF MTG equations were developed to evaluate the effect of the degree of \ncoupling between DOF. Polynomial torque-angle scaling, torque-velocity scaling, and passive functions were defined for the different coupling equations, as well as the activation function. The Biodex System 4 Pro™ was used to determine the net joint torques at the shoulder for 20 participants in isometric, isokinetic, and passive tests. Data was processed and normalized to compare the relative shoulder strength of individuals. MATLAB’s Curve Fitting Toolbox™ was used to find the curves or surfaces that best fit the experimental data for the MTG functions with different degrees of coupling. A completely general model, a female general model, a male general model, and 13 subject-specific models were fit for the three coupling methods. It was found that subject-specific models tended to fit higher-order curves and surfaces compared to the general models that contained averaged data. The models were validated against experimental isokinetic torque data. It was determined that the male general model with position coupling resulted in the lowest error (6.4%), with the position coupling for the completely general model resulting in the next lowest error (8.0%). The female general model resulted in higher errors (average error of 19.9% ± 7.1%), with limited coupling showing the best results with an error of 11.6%. For subject-specific models, it was determined that the average error was the lowest for position and velocity coupling with an error of 22.8% and increasing with decreased coupling. The subject-specific models predicted the general torque trend well for most participants; however, the subject-specific models were highly dependent on the participant’s consistent effort during data collection. The work demonstrated that subject-specific, completely general, female general, and male general MTG models can predict torque results that are dependent on multiple DOF of the shoulder. Future work should include the addition of a fatigue model and the bi-articular nature of the biceps brachii.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».