Journal article

Environmental exposure assessment using indoor/outdoor detection on smartphones

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

Personal and Ubiquitous Computing | SPRINGER LONDON LTD | Published : 2017

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..

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Grants

Awarded by Academy of Finland


Awarded by European Commission


Awarded by Marie Sklodowska-Curie Actions


Funding 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-A71143AKAI), and Marie Sklodowska-Curie Actions (645706-GRAGE).