The Challenges of Musculotendon Forces Estimation in Multiple Muscle Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F05%3A00110308" target="_blank" >RIV/68407700:21220/05:00110308 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The Challenges of Musculotendon Forces Estimation in Multiple Muscle Systems

Popis výsledku v původním jazyce

This study, investigated the practicality, and sensitivity of several different methods of calculating muscle forces during functional activities in humans. Two dynamic systems were chosen, the upper extremity, where the movement flexion/extension elbowjoint was characterized and the lower extremity, where the complex squat motion was studied. The redundant mechanisms were solved using optimization criteria with models of individual muscles according to their active and passive properties. Additionally, determination of muscle forces was examined using an artificial neural network and muscle parameters without apparent dependence between inputs muscle parameters and outputs muscle forces. Moreover, if muscle models with active and passive properties are included in these analyses, it is relatively easy to calculate muscle forces of both agonists and antagonists.

Název v anglickém jazyce

The Challenges of Musculotendon Forces Estimation in Multiple Muscle Systems

Popis výsledku anglicky

This study, investigated the practicality, and sensitivity of several different methods of calculating muscle forces during functional activities in humans. Two dynamic systems were chosen, the upper extremity, where the movement flexion/extension elbowjoint was characterized and the lower extremity, where the complex squat motion was studied. The redundant mechanisms were solved using optimization criteria with models of individual muscles according to their active and passive properties. Additionally, determination of muscle forces was examined using an artificial neural network and muscle parameters without apparent dependence between inputs muscle parameters and outputs muscle forces. Moreover, if muscle models with active and passive properties are included in these analyses, it is relatively easy to calculate muscle forces of both agonists and antagonists.

Druh

O - Ostatní výsledky

CEP obor

EI - Biotechnologie a bionika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2005

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů