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)
Citation
MAAG, Balz; ZHOU, Zimu; and THIELE, Lothar.
W-Air: Enabling personal air pollution monitoring on wearables. (2018). Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2, (1), 24:1-25.
Available at: https://ink.library.smu.edu.sg/sis_research/4693
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3191756