Version 1
: Received: 30 November 2018 / Approved: 3 December 2018 / Online: 3 December 2018 (07:05:04 CET)
How to cite:
Vanus, J.; Kubicek, J.; Gorjani, O.; Koziorek, J. Using the PI ProcessBook to Monitor Activities of Daily Living in Smart Home Care within IoT. Preprints2018, 2018120009. https://doi.org/10.20944/preprints201812.0009.v1
Vanus, J.; Kubicek, J.; Gorjani, O.; Koziorek, J. Using the PI ProcessBook to Monitor Activities of Daily Living in Smart Home Care within IoT. Preprints 2018, 2018120009. https://doi.org/10.20944/preprints201812.0009.v1
Vanus, J.; Kubicek, J.; Gorjani, O.; Koziorek, J. Using the PI ProcessBook to Monitor Activities of Daily Living in Smart Home Care within IoT. Preprints2018, 2018120009. https://doi.org/10.20944/preprints201812.0009.v1
APA Style
Vanus, J., Kubicek, J., Gorjani, O., & Koziorek, J. (2018). Using the PI ProcessBook to Monitor Activities of Daily Living in Smart Home Care within IoT. Preprints. https://doi.org/10.20944/preprints201812.0009.v1
Chicago/Turabian Style
Vanus, J., Ojan Gorjani and Jiri Koziorek. 2018 "Using the PI ProcessBook to Monitor Activities of Daily Living in Smart Home Care within IoT" Preprints. https://doi.org/10.20944/preprints201812.0009.v1
Abstract
This article describes the use of the PI ProcessBook software tool for visualization and indirect monitoring of occupancy of SHC rooms from the measured operational and technical quantities for monitoring of daily living activities for support of independent life of elderly persons. The proposed method 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 implemented, verified and compared in MATLAB SW tool and IBM SPSS SW tool with IoT platform connectivity. Within the proposed method, the Stationary Wavelet Transform de noising algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, 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 persons of the monitored SHC premises for rooms ADL monitoring.
Keywords
smart home care (SHC); monitoring; prediction; trend detection; artificial neural network (ANN), Bayesian regulation method (BRM), wavelet transformation (WT), SPSS (statistical package for the social sciences) IBM; IoT (internet of things), activities of daily living (ADL)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
