Prediction of muscle length during walking by neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F12%3A00201905" target="_blank" >RIV/68407700:21460/12:00201905 - 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

Prediction of muscle length during walking by neural networks

Popis výsledku v původním jazyce

Prediction of the muscle-tendon behavior during walking is nowadays undergoing extensive analysis. The aim of this article is to introduce a possible method of predictions of muscle behavior which can be used for rehabilitation, and also for controllingthe artificial muscles, actuators of prosthesis or rehabilitation facilities of the future. Our work focuses on predicting muscle-tendon lengths during human gait with the use of angle-time diagram. A group of healthy children was measured using a Viconmotion analysis system. The 3D kinematic data were recorded and the OpenSim software system was used to identify the joint angles and muscle length, which are represented by time diagrams. In conjunction with artificial intelligence, time diagrams offera wide area of medical applications. We tested and verified new way of prediction of muscle-tendon length based on neural networks. Artificial neural networks for predicting the muscle behavior learned by time diagrams predicted the muscl

Název v anglickém jazyce

Prediction of muscle length during walking by neural networks

Popis výsledku anglicky

Prediction of the muscle-tendon behavior during walking is nowadays undergoing extensive analysis. The aim of this article is to introduce a possible method of predictions of muscle behavior which can be used for rehabilitation, and also for controllingthe artificial muscles, actuators of prosthesis or rehabilitation facilities of the future. Our work focuses on predicting muscle-tendon lengths during human gait with the use of angle-time diagram. A group of healthy children was measured using a Viconmotion analysis system. The 3D kinematic data were recorded and the OpenSim software system was used to identify the joint angles and muscle length, which are represented by time diagrams. In conjunction with artificial intelligence, time diagrams offera wide area of medical applications. We tested and verified new way of prediction of muscle-tendon length based on neural networks. Artificial neural networks for predicting the muscle behavior learned by time diagrams predicted the muscl

Druh

D - Stať ve sborníku

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 15th Mechatronika 2012

ISBN

978-1-4673-0979-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

Czech Technical University

Místo vydání

Prague

Místo konání akce

Prague

Datum konání akce

5. 12. 2012

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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