Testing of features for fatigue detection in EOG

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F17%3APU124169" target="_blank" >RIV/00216305:26220/17:PU124169 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/17:00097867

Výsledek na webu

<a href="http://content.iospress.com/journals/bio-medical-materials-and-engineering/28/4" target="_blank" >http://content.iospress.com/journals/bio-medical-materials-and-engineering/28/4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/BME-171683" target="_blank" >10.3233/BME-171683</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Testing of features for fatigue detection in EOG

Popis výsledku v původním jazyce

The article deals with the testing of features for fatigue detection in electrooculography (EOG) records. An optimal methodology for EOG signal acquisition is described; the Biopac data acquisition system was used. EOG signals were being recorded while 10 volunteers were watching prepared scenes. Three scenes were created for this purpose – a rotating ball, a video of driving a car, and a cross. Recorded EOG signals were processed and 20 features were extracted. The features involved blinks, slow eye movement (SEM), rapid eye movement (REM), eye instability, magnitude, and periodicity. These features were statistically tested and discussed in terms of fatigue detection ability. Some of the features were compared with published results. Finally, the best features – fatigue indicators – were selected.

Název v anglickém jazyce

Testing of features for fatigue detection in EOG

Popis výsledku anglicky

The article deals with the testing of features for fatigue detection in electrooculography (EOG) records. An optimal methodology for EOG signal acquisition is described; the Biopac data acquisition system was used. EOG signals were being recorded while 10 volunteers were watching prepared scenes. Three scenes were created for this purpose – a rotating ball, a video of driving a car, and a cross. Recorded EOG signals were processed and 20 features were extracted. The features involved blinks, slow eye movement (SEM), rapid eye movement (REM), eye instability, magnitude, and periodicity. These features were statistically tested and discussed in terms of fatigue detection ability. Some of the features were compared with published results. Finally, the best features – fatigue indicators – were selected.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/GAP102%2F11%2F1068" target="_blank" >GAP102/11/1068: Nano-elektro-bio-nástroje pro biochemické a molekulárně-biologické studie eukaryotických buněk (NanoBioTECell)</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

BIO-MEDICAL MATERIALS AND ENGINEERING

ISSN

0959-2989

e-ISSN

1878-3619

Svazek periodika

28

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

379-392

Kód UT WoS článku

000408296300005

EID výsledku v databázi Scopus

2-s2.0-85028679226