Abstract
This article presents a spatiotemporal model of human circadian activity rhythm in smart homes. A spatiotemporal model is used to represent human activity in a time-based system. This article proposes a learning and prediction algorithm to analyze temporal characteristics of the resident's activity. The algorithms combined Allen's temporal logic and Gaussian distribution to incrementally learn and predict next activity of the inhabitant. The methods show 88.1% prediction accuracy when tested with a practical smart home data set. Further analysis showed that human activity in smart homes follows Gaussian distribution, which previously had been merely an assumption.
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