The use of artificial neural networks to predict the muscle behavior.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F13%3A00213798" target="_blank" >RIV/68407700:21460/13:00213798 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15510/13:33145030

Výsledek na webu

<a href="http://link.springer.com/article/10.2478%2Fs13531-012-0067-4" target="_blank" >http://link.springer.com/article/10.2478%2Fs13531-012-0067-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/s13531-012-0067-4" target="_blank" >10.2478/s13531-012-0067-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The use of artificial neural networks to predict the muscle behavior.

Popis výsledku v původním jazyce

The aim of this article is to introduce methods of prediction of muscle behavior of the lower extremities based on artificial neural networks, which can be used for medical purposes. Our work focuses on predicting muscletendon forces and moments during human gait with the use of angle-time diagram. A group of healthy children and children with cerebral palsy were measured using a Vicon MoCap system. The kinematic data was recorded and the OpenSim software system was used to identify the joint angles, muscle-tendon forces and joint muscle moment, which are presented graphically with time diagrams. The musculus gastrocnemius medialis that is often studied in the context of cerebral palsy have been chosen to study the method of prediction. The diagrams ofmean muscle-tendon force and mean moment are plotted and the data about the force-time and moment-time dependencies are used for training neural networks. The new way of prediction of muscle-tendon forces and moments based on neural netw

Název v anglickém jazyce

The use of artificial neural networks to predict the muscle behavior.

Popis výsledku anglicky

The aim of this article is to introduce methods of prediction of muscle behavior of the lower extremities based on artificial neural networks, which can be used for medical purposes. Our work focuses on predicting muscletendon forces and moments during human gait with the use of angle-time diagram. A group of healthy children and children with cerebral palsy were measured using a Vicon MoCap system. The kinematic data was recorded and the OpenSim software system was used to identify the joint angles, muscle-tendon forces and joint muscle moment, which are presented graphically with time diagrams. The musculus gastrocnemius medialis that is often studied in the context of cerebral palsy have been chosen to study the method of prediction. The diagrams ofmean muscle-tendon force and mean moment are plotted and the data about the force-time and moment-time dependencies are used for training neural networks. The new way of prediction of muscle-tendon forces and moments based on neural netw

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EI - Biotechnologie a bionika

OECD FORD obor

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Projekt

<a href="/cs/project/VG20102015002" target="_blank" >VG20102015002: Osobní bezpečnostní dohledový systém pro podporu výcviku a zásahu jednotek IZS</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Central European Journal of Engineering

ISSN

1896-1541

e-ISSN

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Svazek periodika

3

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

9

Strana od-do

410-418

Kód UT WoS článku

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EID výsledku v databázi Scopus

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