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Journal of Sensors
Volume 2015 (2015), Article ID 245498, 10 pages
Research Article

Correcting Air-Pressure Data Collected by MEMS Sensors in Smartphones

1Korea Oceanic and Atmospheric System Technology, No. 1503, 90 Gyeongin-ro 53-gil, Guro-gu, Seoul 152-865, Republic of Korea
2Department of Computer Science and Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 139-701, Republic of Korea
3Department of Computer Engineering, College of Information Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 461-701, Republic of Korea
4Observation Research Division, National Institute of Meteorological Science, 33 Seohobuk-ro, Seogwipo-gi, Jeju-do 697-845, Republic of Korea
5Numerical Data Application Division, National Institute of Meteorological Science, 61 Yeoeuidaebang-ro 16-gil, Dongjak-gu, Seoul 156-720, Republic of Korea

Received 11 May 2015; Revised 10 July 2015; Accepted 14 July 2015

Academic Editor: Geoffrey A. Cranch

Copyright © 2015 Na-Young Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a novel correction method for air-pressure data collected by microelectromechanical pressure sensors embedded in Android-based smartphones, in order to render them usable as meteorological data. The first step of the proposed correction method involves removing the mechanically derived outliers existing beyond the physical limits and those existing outside 3σ, as well as a reduction to the mean sea level pressure using the altitude data from digital elevation models. The second correction step involves classifying data by location and linear-regression analysis utilizing the temperature and humidity sensed by the smartphone to reduce correction errors by performing the analysis according to personalized settings. Air-pressure data obtained from smartphones is subject to several influential factors, depending on the users’ external environment. However, once corrected for spatial location, temperature, and humidity and for individual users after a comprehensive quality control, the corrected air-pressure data was highly reliable as an auxiliary resource for automatic weather stations.