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Bayesian nonparametric modeling of user activities

Human activity modeling is becoming more and more important in ubiquitous computing as it builds a foundation for higher-level applications in areas such as e-health and activity recommendation systems. Many existing works in this area focus on recognizing a pre-defined set of activities using some devices in the supervised learning setting, however, it is hard to define activities and label sensor data, especially for a new environment. In this note we aim to recognize activities in an unsupervised way - segment activity sensor reading sequence and group the segments into meaningful categories by leveraging Sticky HDP-HMM. We have conducted experiments on a sensor dataset collected in an office area using a smartphone and the result shows that our method frees annotation process and renders good activity recognition result.