Daily Routine Classification from Mobile Phone Data

The automatic analysis of real-life, long-term behavior and dynamics of individuals and groups from mobile sensor data constitutes an emerging and challenging domain. We present a framework to classify people's daily routines (defined by day type, and by group affiliation type) from real-life data collected with mobile phones, which include physical location information (derived from cell tower connectivity), and social context (given by person proximity information derived from Bluetooth). We propose and compare single- and multi-modal routine representations at multiple time scales, each capable of highlighting different features from the data, to determine which best characterized the underlying structure of the daily routines. Using a massive data set of 87000+ hours spanning four months of the life of 30 university students, we show that the integration of location and social context and the use of multiple time-scales used in our method is effective, producing accuracies of over 80% for the two daily routine classification tasks investigated, with significant performance differences with respect to the single-modal cues.