Skip to Main Content
This paper attempts to formulate the behavioral pattern of smart homes user activities. Smart homes depend on effective representation of residents' activities into ubiquitous computing elements. User activities inside a home follow specific temporal patterns, which are predictable utilizing statistical analysis. This paper intended to develop a temporal learning algorithm to find out the time difference between residents' activities in smart homes. A temporal algorithm is proposed to incrementally construct a temporal database, which is used to predict the time of next activity of the residents employing central limit theory of statistical probability. The algorithm exhibits 88.3% to 95.3% prediction accuracies for different ranges of mean and standard deviations when verified by practical smart home data. Further stochastic analyses prove that the time difference between the residents' activities follows normal distribution, which was merely an assumption previously.