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Estimating human predictability from mobile sensor data

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5 Author(s)
Jensen, B.S. ; Dept. of Inf. & Math. Modeling, Tech. Univ. of Denmark, Lyngby, Denmark ; Larsen, J.E. ; Jensen, K. ; Larsen, J.
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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.

Published in:

Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on

Date of Conference:

Aug. 29 2010-Sept. 1 2010

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