Learning and predicting multimodal daily life patterns from cell phones

Katayoun Farrahi, Daniel Gatica-Perez
2009 Proceedings of the 2009 international conference on Multimodal interfaces - ICMI-MLMI '09  
In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, and that uses these informative routines to predict unlabeled or missing data. Using a joint representation of location and proximity data over approximately 10 months of 97 individuals' lives, Latent Dirichlet Allocation is applied for the
more » ... upervised learning of topics describing people's most common locations jointly with the most common types of interactions at these locations. We further successfully predict where and with how many other individuals users will be, for people with both highly and lowly varying lifestyles.
doi:10.1145/1647314.1647373 dblp:conf/icmi/FarrahiG09 fatcat:k4w3rtcezjhhxn3upie6mxti6u