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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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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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  • DOI: https://doi.org/10.1007/s00779-017-1028-y

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