Abstract
This paper investigates the prediction of two aspects of human behavior using smartphones as sensing devices. We present a framework for predicting where users will go and which app they will use in the next ten minutes by exploiting the rich contextual information from smartphone sensors. Our first goal is to understand which smartphone sensor data types are important for the two prediction tasks. Secondly, we aim at extracting generic (i.e., user-independent) behavioral patterns and study how generic behavior models can improve the predictive performance of personalized models. Experimental validation was conducted on the Lausanne Data Collection Campaign (LDCC) dataset, with longitudinal smartphone data collected over a period of 17 months from 71 users.
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