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Towards Self-Tracking Personal Pollution Exposure using Wearables

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thesis
posted on 06.08.2019 by Nina Sakhnini
Recent epidemiological studies have shown that long-term exposure to air pollution is positively associated with mild cognitive impairment (MCI). Although interest in pollution monitoring is proliferating, self-tracking personal pollution exposure is little explored. In this thesis, I adopt a human-centered computing approach to explore the design space of personal pollution tracking wearables. This work makes three contributions to human-computer interaction: 1) design guidelines for rapid-prototyping low-cost, sub-optimal personal pollution tracking wearables and a physical prototype that measures pollutants shown to be associated with cognitive impairment in older adults: PM2.5 and ambient noise, 2) exploration of different calibration techniques to improve the accuracy of low-cost PM2.5 sensors, and 3) a characterization of how human interference, our day-to-day activities, significantly affect the operation of personal pollution tracking wearables. In sum, this thesis informs design guidelines about how to physically prototype personal pollution tracking wearables and where to wear them---beyond citizen-science efforts of data collection---rather toward monitoring personal long-term pollution exposures to mitigate the environmental risk factors for many illnesses such as early dementia.

History

Advisor

Chattopadhyay, Debaleena

Chair

Chattopadhyay, Debaleena

Department

Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

Masters

Committee Member

Buy, Ugo Berger-Wolf, Tanya Jones, Rachael M Amft, Oliver

Submitted date

May 2019

Issue date

15/04/2019

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