Human Activity Prediction in Smart Home Time Series Data by using Change Point Detection

Abstract

A Sensor is attached to a house which send message now and then about the housemates or the old people activities. Whether they had taken food in the correct time or not and other activities they perform in the proper time or not. Change Point Detection (CPD) is a matter of perspective which discovers deviating from what are normal or usual changes within the housemates. Any abnormal changes in the housemates have been identifying the presence of time points. The dissimilar changes occurs called SEPERATION Change Point Detection. It will not coincide when a remarkable occurrence of events at any points. Change Point Detection (CPD) occurs at the same time and in the problem of finding unexpected changes in facts and statistics collected together for references and analysis and in the property of the time series changes. An unusual real-time not involving any assumptions as to the form or in the parameters of a frequency distribution change point detection algorithm called Separation , It is used to calculate as a parting to recognize change points in fully measurement characteristics relating to measurements having sufficient depth and substance to be in time series. To ameliorate the order of this algorithm used in ARIMA with SEP algorithm. ARIMA model is used for predicting the Time series forecasting result. If emergency is occur then automatically send notification to caring person. The proposed work can decreasing computational cost and also improves the detection accuracy in the quality or fact of being useful of proposed technique.