USHEr: User separation in home environment

Abstract

With the increase in presence of smart devices in our daily life, it is an important problem for these devices to be more intelligent. The most sought after problems in this area are activity recommendation and prediction. Researchers have proposed solutions for this problem, however, most of them are based on single-user home space. In this paper, we propose an unsupervised approach to separate the logs of multi-user home space into buckets equal to the number of users. With a minimal set of assumptions, the aim of the method is to transform the multi-user problem to a single-user problem. It is achieved by estimating the layout of the house and then tracking the users at room-level. We achieved empirically-determined high precision in estimating the layout and 74% accuracy in separating the multi-user stream.