Prediction of muscle length during walking by neural networks

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Prediction of muscle length during walking by neural networks
Original language description
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
Czech name
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Czech description
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Project
<a href="/en/project/VG20102015002" target="_blank" >VG20102015002: Flexiguard</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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