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Environmental exposure assessment using indoor/outdoor detection on smartphones

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Ordnance Survey, Adanac Drive, SO16 0AS, Southampton, UK

    Theodoros Anagnostopoulos

  2. Center for Ubiquitous Computing University of Oulu, Pentti kaiteran katu 1, P.O. Box 4500, 90014, Oulu, FI, Finland

    Juan Camilo Garcia, Jorge Goncalves, Denzil Ferreira, Simo Hosio & Vassilis Kostakos

  3. School of Computing and Information Systems, The University of Melbourne, VIC, 3010, Melbourne, Australia

    Jorge Goncalves & Vassilis Kostakos

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  1. Theodoros Anagnostopoulos
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Correspondence to Jorge Goncalves.

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Anagnostopoulos, T., Garcia, J.C., Goncalves, J. et al. Environmental exposure assessment using indoor/outdoor detection on smartphones. Pers Ubiquit Comput 21, 761–773 (2017). https://doi.org/10.1007/s00779-017-1028-y

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