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Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F05%3A03113409" target="_blank" >RIV/68407700:21230/05:03113409 - 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

Bio-inspired Methods for Analysis and Classification of Reading Eye Movements of Dyslexic Children.

Popis výsledku v původním jazyce

The paper describes real practical problem of analyzing diagnostic significance of dyslexic eye movements. The biologically inspired methods were used and compared with classical methods of artificial intelligence. Eye movements of 52 school children were measured using videooculographic (VOG) technique, during a reading task. There were three groups of subjects - normal readers, retarded readers and dyslexics. The main goal was to analyze the possibility of dyslexia detection only from the eye movementsignal. Time and frequency domain features were extracted and subset of significant features was chosen by a simple feature selection method. The selected feature subset was visualized using a self-organizing map (SOM). Clusters were formed by the SOM proving that proposed methodology is suitable for automatic dyslexia detection.

Název v anglickém jazyce

Bio-inspired Methods for Analysis and Classification of Reading Eye Movements of Dyslexic Children.

Popis výsledku anglicky

The paper describes real practical problem of analyzing diagnostic significance of dyslexic eye movements. The biologically inspired methods were used and compared with classical methods of artificial intelligence. Eye movements of 52 school children were measured using videooculographic (VOG) technique, during a reading task. There were three groups of subjects - normal readers, retarded readers and dyslexics. The main goal was to analyze the possibility of dyslexia detection only from the eye movementsignal. Time and frequency domain features were extracted and subset of significant features was chosen by a simple feature selection method. The selected feature subset was visualized using a self-organizing map (SOM). Clusters were formed by the SOM proving that proposed methodology is suitable for automatic dyslexia detection.

Druh

A - Audiovizuální tvorba

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2005

Kód důvěrnosti údajů

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

ISBN

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Místo vydání

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Název nakladatele resp. objednatele

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Verze

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Identifikační číslo nosiče

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