Using Wavelet Transformation for Prediction CO2in Smart Home Care Within IoT for Monitor Activities of Daily Living

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242727" target="_blank" >RIV/61989100:27240/19:10242727 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-28374-2_43" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-28374-2_43</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-28374-2_43" target="_blank" >10.1007/978-3-030-28374-2_43</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Wavelet Transformation for Prediction CO2in Smart Home Care Within IoT for Monitor Activities of Daily Living

Popis výsledku v původním jazyce

In Smart Home Care (SHC) rooms from the measured operational and technical quantities for monitoring activities of every day of life for support of independent life for elderly people. The proposed algorithm for data processing (predicting the CO2course using neural networks from the measured temperature indoor Ti(oC), temperature outdoor To(oC) and the relative humidity indoor rHi (%)) was applicated, verified and compared in MATLAB SW tool and IBM SPSS SW tool with IoT platform connectivity. In the proposed method, a stationary wavelet transformation algorithm was used to remove the noise of the resulting predicted waveform of expected process. Two long-term experiments were performed (specifically from February 8 to February 15, 2015, from June 8 to June 15, 2015) and two short-term experiments (from February 8, 2015 and from June 8, 2015). For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 90%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored SHC premises for rooms ADL monitoring. (C) 2019, Springer Nature Switzerland AG.

Název v anglickém jazyce

Using Wavelet Transformation for Prediction CO2in Smart Home Care Within IoT for Monitor Activities of Daily Living

Popis výsledku anglicky

In Smart Home Care (SHC) rooms from the measured operational and technical quantities for monitoring activities of every day of life for support of independent life for elderly people. The proposed algorithm for data processing (predicting the CO2course using neural networks from the measured temperature indoor Ti(oC), temperature outdoor To(oC) and the relative humidity indoor rHi (%)) was applicated, verified and compared in MATLAB SW tool and IBM SPSS SW tool with IoT platform connectivity. In the proposed method, a stationary wavelet transformation algorithm was used to remove the noise of the resulting predicted waveform of expected process. Two long-term experiments were performed (specifically from February 8 to February 15, 2015, from June 8 to June 15, 2015) and two short-term experiments (from February 8, 2015 and from June 8, 2015). For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 90%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored SHC premises for rooms ADL monitoring. (C) 2019, Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 11684

ISBN

978-3-030-28373-5

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

10

Strana od-do

500-509

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Hendaye

Datum konání akce

4. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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