Estimating human predictability from mobile sensor data

Sand Jensen, B. , Larsen, J. E., Jensen, K., Larsen, J. and Hansen, L. K. (2010) Estimating human predictability from mobile sensor data. In: 2010 IEEE International Workshop on Machine Learning for Signal Processing, Kittilä, Finland, 29 Aug - 01 Sep 2010, pp. 196-201. ISBN 9781424478750 (doi:10.1109/MLSP.2010.5588997)

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Quantification of human behavior is of prime interest in many applications ranging from behavioral science to practical applications like GSM resource planning and context-aware services. As proxies for humans, we apply multiple mobile phone sensors all conveying information about human behavior. Using a recent, information theoretic approach it is demonstrated that the trajectories of individual sensors are highly predictable given complete knowledge of the infinite past. We suggest using a new approach to time scale selection which demonstrates that participants have even higher predictability of non-trivial behavior on smaller timer scale than previously considered.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Jensen, Dr Bjorn
Authors: Sand Jensen, B., Larsen, J. E., Jensen, K., Larsen, J., and Hansen, L. K.
College/School:College of Science and Engineering > School of Computing Science
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