Hand gesture recognition system using single-mixture source separation and flexible neural trees

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092823" target="_blank" >RIV/61989100:27240/14:86092823 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1177/1077546313481001" target="_blank" >http://dx.doi.org/10.1177/1077546313481001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/1077546313481001" target="_blank" >10.1177/1077546313481001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hand gesture recognition system using single-mixture source separation and flexible neural trees

Popis výsledku v původním jazyce

Surface Electromyography (sEMG) is widely used in evaluating the functional status of hands to assist in hand gesture recognition in many fields of treatment and rehabilitation. Multi-channel parallel interfaces (MCPIs) or time-division multiple access (TDMA) interfaces are the main technologies for the man-machine communication medium of sEMG recognition instruments. However, they can also result in a complex circuit connection and noise interference. A hand gesture recognition model based on sEMG signals by using single-mixture source separation and flexible neural trees (FNTs) is a breakthrough model of hand gesture recognition designed to conquer the above defects. It distinguishes itself from the traditional MCPI or TDMA interfaces by more accurate and reliable measurements of signals. Single-mixture source separation by use of ensemble empirical mode decomposition (EEMD), principal component analysis (PCA) and independent component analysis (ICA) is a novel single-input multiple-

Název v anglickém jazyce

Hand gesture recognition system using single-mixture source separation and flexible neural trees

Popis výsledku anglicky

Surface Electromyography (sEMG) is widely used in evaluating the functional status of hands to assist in hand gesture recognition in many fields of treatment and rehabilitation. Multi-channel parallel interfaces (MCPIs) or time-division multiple access (TDMA) interfaces are the main technologies for the man-machine communication medium of sEMG recognition instruments. However, they can also result in a complex circuit connection and noise interference. A hand gesture recognition model based on sEMG signals by using single-mixture source separation and flexible neural trees (FNTs) is a breakthrough model of hand gesture recognition designed to conquer the above defects. It distinguishes itself from the traditional MCPI or TDMA interfaces by more accurate and reliable measurements of signals. Single-mixture source separation by use of ensemble empirical mode decomposition (EEMD), principal component analysis (PCA) and independent component analysis (ICA) is a novel single-input multiple-

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

JVC/Journal of Vibration and Control

ISSN

1077-5463

e-ISSN

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Svazek periodika

20

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

1333-1342

Kód UT WoS článku

000338722600006

EID výsledku v databázi Scopus

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