The Journal of Machine Learning Research
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
A location predictor based on dependencies between multiple lifelog data
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
What is happening now? Detection of activities of daily living from simple visual features
Personal and Ubiquitous Computing
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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 unsupervised 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.