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, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´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 CO 2 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 CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 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 (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>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 CO 2 for ADL monitoring in SHC.

The internet protocol television service (IPTV) has become a key product for internet service providers (ISP), offering several benefits to both ISP and end-users. Because packet networks based on internet protocol have not been prepared for time-sensitive services, such as voice or video, packet networks have had to adopt several mechanisms to secure minimal transmission standards in the form of data stream prioritization. There are two commonly used approaches for video quality assessment. The first approach needs an original source for comparison (full-reference objective metrics), and the second one requires observers for subjective evaluation of video quality. Both approaches are impractical in real-time transmission because it is difficult to transform an objective score into a subjective quality perception, and on the other hand, subjective tests are not able to be performed immediately. Since many countries worldwide put IPTV on the same level as other broadcasting systems (e.g., terrestrial, cable, or satellite), IPTV services are subject to regulation by the national regulation authority. This results in the need to prepare service qualitative criteria and monitoring tools capable of measuring end-user satisfaction levels. Our proposed model combines the principles of both assessment approaches, which results in an effective monitoring solution. Therefore, the main contribution of the created system is to offer a monitoring tool able to analyze the features extracted from the video sequence and transmission system and promptly translate their impact into a subjective point of view.

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Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO2 predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO2 levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO2 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%.

This article describes the design and verification of the indirect method of predicting the course of CO 2 concentration (ppm) from the measured temperature variables T indoor (°C) and the relative humidity rH indoor (%) and the temperature T outdoor (°C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter 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 1 to

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This publication describes an original simple low-cost MR fully-compatible and safe fiber-optic breathing sensor (FOBS), which can be used for respiratory triggering and for monitoring the development of respiratory rate within the MR environment and can, thus, serve as prevention from the hyperventilation syndrome. The sensor is created by encapsulation of the Bragg grating into conventional nasal oxygen cannulas. The sensor is immune to minor patient movements, thus limiting movement artifacts to a minimum. Thanks to this fact it can be used for the retrospective/prospective respiratory gating. The sensor is immune to electromagnetic interference (EMI) and can thus be used in any magnetic field (1.5T, 3T, and 7T). The sensor prototype has been tested in both laboratory and real magnetic resonance (3T) environments relative to conventional pneumatic respiration references (PRR). The data measured were statistically evaluated using the objective Bland-Altman method (BAM) and the functionality of the proposed solution was confirmed. Respiratory Triggering functionality was confirmed by the radiologic doctors on the basis of analyzing images using the most used respiratory triggered T2 TSE 3D sequences and by objective method using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). INDEX TERMS Magnetic resonance imaging (MRI), respiratory rate (RR), fiber-optic sensor, fiber Bragg grating (FBG), respiratory triggering.