Using Wavelet Transformation for Prediction CO2 in Smart Home Care Within IoT for Monitor Activities of Daily Living

In Smart Home Care (SHC) rooms from the measured operational and technical quantities for monitoring activities of every day of life for support of independent life for elderly people. The proposed algorithm for data processing (predicting the CO2 course using neural networks from the measured temperature indoor Ti (°C), temperature outdoor To (°C) and the relative humidity indoor rHi (%)) was applicated, verified and compared in MATLAB SW tool and IBM SPSS SW tool with IoT platform connectivity. In the proposed method, a stationary wavelet transformation algorithm was used to remove the noise of the resulting predicted waveform of expected process. Two long-term experiments were performed (specifically from February 8 to February 15, 2015, from June 8 to June 15, 2015) and two short-term experiments (from February 8, 2015 and from June 8, 2015). For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 90%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored SHC premises for rooms ADL monitoring.

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