Modelling smartphone usage : a Markov state transition model

Kostakos, Vassilis; Ferreira, Denzil; Goncalves, Jorge; Hosio, Simo (2016-09-12)
 
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https://doi.org/10.1145/2971648.2971669

Kostakos, Vassilis
Ferreira, Denzil
Goncalves, Jorge
Hosio, Simo
Association for Computing Machinery
12.09.2016

Vassilis Kostakos, Denzil Ferreira, Jorge Goncalves, and Simo Hosio. 2016. Modelling smartphone usage: a markov state transition model. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '16). ACM, New York, NY, USA, 486-497. DOI: https://doi.org/10.1145/2971648.2971669

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© Copyright is held by the owner/author(s). Publication rights licensed to ACM. 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '16), https://doi.org/10.1145/2971648.2971669.
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doi:https://doi.org/10.1145/2971648.2971669
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https://urn.fi/URN:NBN:fi-fe201901212616
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Abstract

We develop a Markov state transition model of smartphone screen use. We collected use traces from real-world users during a 3-month naturalistic deployment via an app-store. These traces were used to develop an analytical model which can be used to probabilistically model or predict, at runtime, how a user interacts with their mobile phone, and for how long. Unlike classification-driven machine learning approaches, our analytical model can be interrogated under unlimited conditions, making it suitable for a wide range of applications including more realistic automated testing and improving operating system management of resources.

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