It is important for older and disabled people who live alone to be able to cope with the daily challenges of living at home. In order to support independent living, the Smart Home Care (SHC) concept offers the possibility of providing comfortable control of operational and technical functions using a mobile robot for operating and assisting activities to support independent living for elderly and disabled people. This article presents a unique proposal for the implementation of interoperability between a mobile robot and KNX technology in a home environment within SHC automation to determine the presence of people and occupancy of occupied spaces in SHC using measured operational and technical variables (to determine the quality of the indoor environment), such as temperature, relative humidity, light intensity, and CO2 concentration, and to locate occupancy in SHC spaces using magnetic contacts monitoring the opening/closing of windows and doors by indirectly monitoring occupancy without the use of cameras. In this article, a novel method using nonlinear autoregressive Neural Networks (NN) with exogenous inputs and nonlinear autoregressive is used to predict the CO2 concentration waveform to transmit the information from KNX technology to mobile robots for monitoring and determining the occupancy of people in SHC with better than 98% accuracy.

• Klíčová slova
• KNX technology, Smart Home Care, localization, mobile robot, occupancy, presence,
• MeSH
• lidé MeSH
• oxid uhličitý MeSH
• robotika * metody   MeSH
• samostatný způsob života MeSH
• senioři MeSH
• služby domácí péče * MeSH
• technologie MeSH
• Check Tag
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• oxid uhličitý MeSH

The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing.

• Klíčová slova
• Activities monitoring with indirect methods, Big data processing, Prediction of room occupancy, Presence of person monitoring, Smart home,
• Publikační typ
• časopisecké články MeSH

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

• Klíčová slova
• artificial neural network (ANN), human activity recognition, intelligent buildings (IB), smart home (SH),
• MeSH
• automatizace MeSH
• bydlení MeSH
• lidé MeSH
• lidské činnosti * MeSH
• neuronové sítě * MeSH
• zápěstí MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

• Klíčová slova
• activity recognition, artificial neural network, classification, deep learning, intelligent buildings, logistic regression, prediction, smart homes,
• MeSH
• klimatizace MeSH
• lidé MeSH
• lidské činnosti * MeSH
• logistické modely MeSH
• neuronové sítě MeSH
• nositelná elektronika * MeSH
• oxid uhličitý MeSH
• senioři MeSH
• teplota MeSH
• větrání MeSH
• vlhkost MeSH
• vytápění MeSH
• znečištění ovzduší MeSH
• Check Tag
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• oxid uhličitý MeSH

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

• Klíčová slova
• Internet of Things, artificial neural network, cloud computing, intelligent buildings, multilayer perceptron, prediction, smart home, wavelet transformation,
• Publikační typ
• časopisecké články MeSH

This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an artificial neural network (ANN) with a scaled conjugate gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the adaptive filtration with an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

• Klíčová slova
• artificial neural network (ANN), fiber bragg grating (FBG), number of person recognition, occupancy, prediction, scaled conjugate gradient (SCG), smart home (SH),
• Publikační typ
• časopisecké články MeSH