Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2018

Abstract

Accurate, portable and personal air pollution sensing devices enable quantification of individual exposure to air pollution, personalized health advice and assistance applications. Wearables are promising (e.g., on wristbands, attached to belts or backpacks) to integrate commercial off-the-shelf gas sensors for personal air pollution sensing. Yet previous research lacks comprehensive investigations on the accuracies of air pollution sensing on wearables. In response, we proposed W-Air, an accurate personal multi-pollutant monitoring platform for wearables. We discovered that human emissions introduce non-linear interference when low-cost gas sensors are integrated into wearables, which is overlooked in existing studies. W-Air adopts a sensor-fusion calibration scheme to recover high-fidelity ambient pollutant concentrations from the human interference. It also leverages a neural network with shared hidden layers to boost calibration parameter training with fewer measurements and utilizes semi-supervised regression for calibration parameter updating with little user intervention. We prototyped W-Air on a wristband with low-cost gas sensors. Evaluations demonstrated that W-Air reports accurate measurements both with and without human interference and is able to automatically learn and adapt to new environments.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Volume

2

Issue

1

First Page

24:1

Last Page

25

ISSN

2474-9567

Identifier

10.1145/3191756

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3191756

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