Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.513514
Title: Probabilistic modelling and inference of human behaviour from mobile phone time series
Author: Choujaa, Driss
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2009
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Abstract:
With an estimated 4.1 billion subscribers around the world, the mobile phone offers a unique opportunity to sense and understand human behaviour from location, co-presence and communication data. While the benefit of modelling this unprecedented amount of data is widely recognised, a number of challenges impede the development of accurate behaviour models. In this thesis, we identify and address two modelling problems and show that their consideration improves the accuracy of behaviour inference. We first examine the modelling of long-range dependencies in human behaviour. Human behaviour models only take into account short-range dependencies in mobile phone time series. Using information theory, we quantify long-range dependencies in mobile phone time series for the first time, demonstrate that they exhibit periodic oscillations and introduce novel tools to analyse them. We further show that considering what the user did 24 hours earlier improves accuracy when predicting user behaviour five hours or longer in advance. The second problem that we address is the modelling of temporal variations in human behaviour. The time spent by a user on an activity varies from one day to the next. In order to recognise behaviour patterns despite temporal variations, we establish a methodological connection between human behaviour modelling and biological sequence alignment. This connection allows us to compare, cluster and model behaviour sequences and introduce novel features for behaviour recognition which improve its accuracy. The experiments presented in this thesis have been conducted on the largest publicly available mobile phone dataset labelled in an unsupervised fashion and are entirely repeatable. Furthermore, our techniques only require cellular data which can easily be recorded by today's mobile phones and could benefit a wide range of applications including life logging, health monitoring, customer profiling and large-scale surveillance.
Supervisor: Dulay, Naranker Sponsor: EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.513514  DOI: Not available
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