Flexible Neural Trees for Online Hand Gesture Recognition using Surface Electromyography
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86092951" target="_blank" >RIV/61989100:27240/12:86092951 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.4304/jcp.7.5.1099-1103" target="_blank" >http://dx.doi.org/10.4304/jcp.7.5.1099-1103</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4304/jcp.7.5.1099-1103" target="_blank" >10.4304/jcp.7.5.1099-1103</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Flexible Neural Trees for Online Hand Gesture Recognition using Surface Electromyography
Popis výsledku v původním jazyce
Normal hand gesture recognition methods using surface Electromyography (sEMG) signals require designers to use digital signal processing hardware or ensemble methods as tools to solve real time hand gesture classification. Some methods could also resultin complicated computational models, complex circuit connection and lower online recognition rate. It is therefore imperative to have good methods to explore a more suitable online design choice, which can avoid the problems mentioned above. An online hand gesture recognition model by using Flexible Neural Trees (FNT) and based on sEMG signals is proposed in this paper. The sEMG is a non-invasive, easy to record signal of superficial muscles from the skin surface, which has been applied in many fields of treatment and rehabilitation. The FNT model is generated and evolved based on the pre-defined simple instruction sets, which can solve highly structure dependent problem of the Artificial Neural Network (ANN). FNT method avoids complica
Název v anglickém jazyce
Flexible Neural Trees for Online Hand Gesture Recognition using Surface Electromyography
Popis výsledku anglicky
Normal hand gesture recognition methods using surface Electromyography (sEMG) signals require designers to use digital signal processing hardware or ensemble methods as tools to solve real time hand gesture classification. Some methods could also resultin complicated computational models, complex circuit connection and lower online recognition rate. It is therefore imperative to have good methods to explore a more suitable online design choice, which can avoid the problems mentioned above. An online hand gesture recognition model by using Flexible Neural Trees (FNT) and based on sEMG signals is proposed in this paper. The sEMG is a non-invasive, easy to record signal of superficial muscles from the skin surface, which has been applied in many fields of treatment and rehabilitation. The FNT model is generated and evolved based on the pre-defined simple instruction sets, which can solve highly structure dependent problem of the Artificial Neural Network (ANN). FNT method avoids complica
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
IN - Informatika
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2012
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Journal of Computers
ISSN
1796-203X
e-ISSN
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Svazek periodika
7
Číslo periodika v rámci svazku
5
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
5
Strana od-do
1099-1103
Kód UT WoS článku
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EID výsledku v databázi Scopus
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