The Challenges of Musculotendon Forces Estimation in Multiple Muscle Systems
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F05%3A00113157" target="_blank" >RIV/68407700:21220/05:00113157 - isvavai.cz</a>
Výsledek na webu
—
DOI - Digital Object Identifier
—
Alternativní jazyky
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.
Klasifikace
Druh
C - Kapitola v odborné knize
CEP obor
EI - Biotechnologie a bionika
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
Z - Vyzkumny zamer (s odkazem do CEZ)
Ostatní
Rok uplatnění
2005
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název knihy nebo sborníku
Mechanist´s Jotter 2005
ISBN
80-01-03199-3
Počet stran výsledku
55
Strana od-do
83-137
Počet stran knihy
168
Název nakladatele
ČVUT FS, Ústav mechaniky
Místo vydání
Praha
Kód UT WoS kapitoly
—