In order to provide a user-friendly environment for the management of the operational and technical functions along with providing support for the independent housing of senior citizens and disabled persons in buildings indicated as Smart House Care (SHC), it is necessary to make appropriate visualization of the technological process as required by the users with the possibility of the indirect monitoring of the seniors’ life activities based on the information obtained from the sensors used for the common management of the operational and technical functions in SHC. Privacy, reliability and false alarms are the main challenges to be considered for the development of efficient systems to detect and classify the Activities of Daily Living (ADL) and Falls. The system monitors actions taken by the residents and looks for patterns in the environment which reliably predict these actions, where a neural network learns these patterns and the system then performs the learned actions automatically for improving the Quality of Life (QoL). The researchers point out that the Adaptive House (the concept of a home which programs itself), Learning Homes and Attentive Homes must be programmed for a particular family and home and updated in line with changes in their lifestyle. Intelligent buildings respond to the needs of occupants and society, promoting the well-being of those living and working in them. An intelligent building requires real-time information about its occupants so that it can continually adapt and respond. The prediction accuracy achieved in the selected experiments was greater than 95%.Īn intelligent building is one that is responsive to the requirements of occupants, organizations, and society. To increase the accuracy of CO 2 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The most accurately predicted results were obtained from data processed at a daily interval. The Radial Basis Function (RBF) method was applied to predict CO 2 levels from the measured indoor and outdoor temperatures and relative humidity. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods.
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The paper examines the possibilities of increasing the accuracy of CO 2 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. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms.