To monitor and visualize the operational and technical functions in Smart Home Care (SHC), it is necessary to use a robust SW tool to provide user-friendly display of the measured quantities with the ability of well-arranged preparation of the acquired data for further processing and analysis. This article describes the use of the PI (Plant Information enterprise information system) System software tool for visualization and indirect monitoring of occupancy of SHC rooms from the measured operational and technical quantities. Within the further processing of the measured data, the method of predicting the CO₂ waveform using neural networks from the measured temperature indoor T_i (°C) and the relative humidity indoor rH_i (%) with the subsequent use of trend signal detection based on wavelet transform is devised.