Dirichlet Process Gaussian Mixture Model for Activity Discovery in Smart Homes with Ambient Sensors

Existing approaches to activity recognition in smart homes mostly rely on supervised learning from well-annotated sensor data, acquired in a controled lab environment. However obtaining such labeled data in real home scenarios could be prohibitive due to either the privacy concerns of using cameras, or the low adherence of self reports done by home residents. Unsupervised learning, on the other hand, aims at discovering activities through applying fixed complexity models, yet assuming apriori knowledge of the number of activities. Again this is also a non-practical approach because the number of activities could vary drastically, even within a home. In this paper, we propose a novel practical unsupervised Bayesian nonparametric model to discover activities in smart homes, without prior assumption on the number of activities. Instead, our model can automatically infer such number only from sensor readings, thus it can be easily applied to any new home. We test our method on a public dataset and a dataset collected in our project. On the CASAS dataset, which has activity labels, our approach can achieve the performance close to the best of GMM and outperforms K-means. On our smart home dataset, the discovered activities are highly correlated with the typical daily routine of the resident. Such experimental results demonstrate the efficiency of our method for activity discovery in smart homes.