Online learning with mobile sensor data for user recognition

Abstract

Currently, mobile devices built with powerful embedded sensors create new opportunities for data mining applications such as monitoring user activity. In this paper, we target at user recognition based on sensor data of remote control, in which activity recognition determines a user's action that is in favor of collecting one's individual sensor data to identify different users. This new problem faces two challenges: first, sensor data is sensitive and constantly changing which is difficult to obtain meaningful features; second, streaming sensor data for online learning is usually imbalanced on which traditional classifiers are not well performed. To address these challenges, we introduce an efficient activity recognition algorithm by exploring the physical appearance of sensor data, and then an online incremental classifier to deal with imbalanced data streams by adaptively generating training data. Extensive online and offline experiments demonstrate that our proposed method outperforms state-of-the-art algorithms in terms of accuracy.