Monitoring of the daily living activities in smart home care

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238485" target="_blank" >RIV/61989100:27240/17:10238485 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1186/s13673-017-0113-6" target="_blank" >https://link.springer.com/article/10.1186/s13673-017-0113-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13673-017-0113-6" target="_blank" >10.1186/s13673-017-0113-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Monitoring of the daily living activities in smart home care

Popis výsledku v původním jazyce

One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO2, humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO2 concentration measurement for detecting and monitoring room&apos;s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO2 concentrations from obtained measurements of temperature (indoor, outdoor) T-i, T-o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO2, T and rH with the aim to indirectly predict CO2 leading to the elimination of CO2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (&gt;95%) within the short-term and long-term experiments for learned ANN (1.6.2015-30.6.2015). For learned ANN (1.2.2014-27.2.2014) was verified worse method accuracy (&gt;60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO2 for ADL monitoring in SHC.

Název v anglickém jazyce

Monitoring of the daily living activities in smart home care

Popis výsledku anglicky

One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO2, humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO2 concentration measurement for detecting and monitoring room&apos;s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO2 concentrations from obtained measurements of temperature (indoor, outdoor) T-i, T-o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO2, T and rH with the aim to indirectly predict CO2 leading to the elimination of CO2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (&gt;95%) within the short-term and long-term experiments for learned ANN (1.6.2015-30.6.2015). For learned ANN (1.2.2014-27.2.2014) was verified worse method accuracy (&gt;60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO2 for ADL monitoring in SHC.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Human-centric Computing and Information Sciences

ISSN

2192-1962

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

December 2017, 7:30

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

34

Strana od-do

—

Kód UT WoS článku

000414417400001

EID výsledku v databázi Scopus

2-s2.0-85033224975