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Using Wavelet Transformation for Prediction CO2 in Smart Home Care Within IoT for Monitor Activities of Daily Living

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Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11684))

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Abstract

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 CO2 course using neural networks from the measured temperature indoor Ti (°C), temperature outdoor To (°C) 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.

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Acknowledgment

The work and the contributions were supported by the project SV450994 Biomedicínské inženýrské systémy XV’. This study was also supported by the research project The Czech Science Foundation (GACR) 2017 No. 17-03037S Investment evaluation of medical device development run at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. This study was supported by the research project The Czech Science Foundation (TACR) ETA No. TL01000302 Medical Devices development as an effective investment for public and private entities.

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Authors and Affiliations

  1. Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, 17. Listopadu 15, Ostrava, Czech Republic

    Jan Vanus, Alice Krestanova, Jan Kubicek, Ojan Gorjani, Marek Penhaker & David Oczka

Authors
  1. Jan Vanus
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  2. Alice Krestanova
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  3. Jan Kubicek
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  4. Ojan Gorjani
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  5. Marek Penhaker
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  6. David Oczka
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Corresponding author

Correspondence to Jan Kubicek .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l'Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l'Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l'Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Vanus, J., Krestanova, A., Kubicek, J., Gorjani, O., Penhaker, M., Oczka, D. (2019). Using Wavelet Transformation for Prediction CO2 in Smart Home Care Within IoT for Monitor Activities of Daily Living. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_43

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  • DOI: https://doi.org/10.1007/978-3-030-28374-2_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28373-5

  • Online ISBN: 978-3-030-28374-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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