Personal and Ubiquitous Computing

, Volume 21, Issue 4, pp 761–773 | Cite as

Environmental exposure assessment using indoor/outdoor detection on smartphones

  • Theodoros Anagnostopoulos
  • Juan Camilo Garcia
  • Jorge GoncalvesEmail author
  • Denzil Ferreira
  • Simo Hosio
  • Vassilis Kostakos
Original Article


We present an energy-efficient method for Indoor/Outdoor detection on smartphones. The creation of an accurate environmental exposure detection method enables crucial advances to a number of health sciences, which seek to model patients’ environmental exposure. In a field trial, we collected data from multiple smartphone sensors, along with explicit indoor/outdoor labels entered by participants. Using this rich dataset, we evaluate multiple classification models, optimised for accuracy and low energy consumption. Using all sensors, we can achieve 99% classification accuracy. Using only a subset of energy-efficient sensors we achieve 92.91% accuracy. We systematically quantify how subsampling can be used as a trade-off for accuracy and energy consumption. Our work enables researchers to quantify environmental exposure using commodity smartphones.


Energy efficiency Environmental exposure Indoor/outdoor detection Smartphones 



This work is partially funded by the Academy of Finland (Grants 276786-AWARE, 286386-CPDSS, 285459-iSCIENCE, 304925-CARE), the European Commission (Grant 6AIKA-A71143-AKAI), and Marie Skłodowska-Curie Actions (645706-GRAGE).


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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Theodoros Anagnostopoulos
    • 1
  • Juan Camilo Garcia
    • 2
  • Jorge Goncalves
    • 2
    • 3
    Email author
  • Denzil Ferreira
    • 2
  • Simo Hosio
    • 2
  • Vassilis Kostakos
    • 2
    • 3
  1. 1.Ordnance SurveySouthamptonUK
  2. 2.Center for Ubiquitous Computing University of OuluOuluFinland
  3. 3.School of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

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