SAKHNINI-THESIS-2019.pdf (6.51 MB)
Towards Self-Tracking Personal Pollution Exposure using Wearables
thesis
posted on 2019-08-06, 00:00 authored by Nina SakhniniRecent 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, DebaleenaChair
Chattopadhyay, DebaleenaDepartment
Computer ScienceDegree Grantor
University of Illinois at ChicagoDegree Level
- Masters
Committee Member
Buy, Ugo Berger-Wolf, Tanya Jones, Rachael M Amft, OliverSubmitted date
May 2019Issue date
2019-04-15Usage metrics
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