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Wearable sensors for multifactorial personal exposure measurements - A ranking study.

Authors :
Ueberham M
Schlink U
Source :
Environment international [Environ Int] 2018 Dec; Vol. 121 (Pt 1), pp. 130-138. Date of Electronic Publication: 2018 Sep 08.
Publication Year :
2018

Abstract

Individuals are simultaneously exposed to multiple environmental stressors during their daily life. Studies of adverse health effects and their etiology as well as recommendations for a healthier life style demand for an assessment of multifactorial personal exposure, according to the exposome concept. A challenge is to record exposure while people are moving in heterogeneous urban environments. Therefore wearable sensor technologies are becoming a promising way to measure personal exposure continuously: indoors, outdoors and even on the move. So far, studies which test the accuracy and usability of wearable sensors for multiple stressors are lacking. Performance evaluations are important and should take place beforehand, especially to ensure the success of citizens-oriented studies. For the first time we rigorously examined the accuracy and application suitability of wearable sensors for acoustic noise, heat (temp), particle number counts (PNC) and geo-location (GPS) in different environments. We present an extensive device inter-comparison and a ranking of the sensors based on performance measures, Taylor diagrams, Bland-Altman plots, and ease-of-use aspects. The sensors showed moderate to high correlations with precision reference devices (r = 0.4-0.99). Differences between errors outdoors and indoors suggest that environmental conditions have impact upon the accuracy of the sensors. Reaction time, recording interval, and sensor ventilation are features that play a crucial role for both ease-of-use and accuracy. We conclude with a final performance () ranking: (GPS) >  (noise) >  (temp) >  (PNC). The results are relevant for future epidemiological studies of multifactorial exposure of individuals and their health and should guide the selection of wearables when persons are involved that are technically untaught. Inferences from multifactorial data are based on the performance of all sensors and the weakest chain links are PNC and temp sensors for which our article recommends urgent improvements.<br /> (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1873-6750
Volume :
121
Issue :
Pt 1
Database :
MEDLINE
Journal :
Environment international
Publication Type :
Academic Journal
Accession number :
30199668
Full Text :
https://doi.org/10.1016/j.envint.2018.08.057