Energy-efficient prediction of smartphone unlocking
Luo, Chu; Visuri, Aku; Klakegg, Simon; van Berkel, Niels; Sarsenbayeva, Zhanna; Möttönen, Antti; Goncalves, Jorge; Anagnostopoulos, Theodoros; Ferreira, Denzil; Flores, Huber; Velloso, Eduardo; Kostakos, Vassilis (2018-12-12)
Luo, C., Visuri, A., Klakegg, S. et al. Pers Ubiquit Comput (2019) 23: 159. https://doi.org/10.1007/s00779-018-01190-0
© Springer-Verlag London Ltd., part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Personal and Ubiquitous Computing. The final authenticated version is available online at: https://doi.org/10.1007/s00779-018-01190-0.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe201903077772
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Abstract
We investigate the predictability of the next unlock event on smartphones, using machine learning and smartphone contextual data. In a 2-week field study with 27 participants, we demonstrate that it is possible to predict when the next unlock event will occur. Additionally, we show how our approach can improve accuracy and energy efficiency by solely relying on software-related contextual data. Based on our findings, smartphone applications and operating systems can improve their energy efficiency by utilising short-term predictions to minimise unnecessary executions, or launch computation-intensive tasks, such as OS updates, in the locked state. For instance, by inferring the next unlock event, smartphones can pre-emptively collect sensor data or prepare timely content to improve the user experience during the subsequent phone usage session.
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