Measurement system for classification of hand's gesture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241564" target="_blank" >RIV/61989100:27240/18:10241564 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/HealthCom.2018.8531079" target="_blank" >http://dx.doi.org/10.1109/HealthCom.2018.8531079</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/HealthCom.2018.8531079" target="_blank" >10.1109/HealthCom.2018.8531079</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Measurement system for classification of hand's gesture

Popis výsledku v původním jazyce

The goal was to create precise hardware that would be able to measure signal of myopotentials from defined area of forearm for the computer analysis without external noise and with right amplification. The second goal was to program an algorithm which could classify specific gestures of hand based on an analy17sis of myopotencial signals. The computer software was programmed in C# programming language. Signal processing and drawing to user interface was in real time. The one of five possible gestures that user made was analysed by using fuzzy logic and designed system of scaling. It was developed fuzzy classification which is able to recognize gestures with high accuracy. (C) 2018 IEEE.

Název v anglickém jazyce

Measurement system for classification of hand's gesture

Popis výsledku anglicky

The goal was to create precise hardware that would be able to measure signal of myopotentials from defined area of forearm for the computer analysis without external noise and with right amplification. The second goal was to program an algorithm which could classify specific gestures of hand based on an analy17sis of myopotencial signals. The computer software was programmed in C# programming language. Signal processing and drawing to user interface was in real time. The one of five possible gestures that user made was analysed by using fuzzy logic and designed system of scaling. It was developed fuzzy classification which is able to recognize gestures with high accuracy. (C) 2018 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services, Healthcom 2018

ISBN

978-1-5386-4294-8

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Ostrava

Datum konání akce

17. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—