University of Illinois at Chicago
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Deep Learning Inference of Personalized Air Pollution Intake

thesis
posted on 2022-08-01, 00:00 authored by Luca Dibattista
Air pollution is the world’s largest single environmental health risk according to the World Health Organization. Data about the pollution is publicly available in almost any location. Companies such as Google and Apple have started showing the pollution information in the weather app of Android and iOS devices. The pollution information that they provide is usually the concentration level of a certain pollutant, or an index that makes easier for people to understand the risk level. The problem with this method is that it gives the risk level considering just the pollutant(s) concentration but does not involve any information about the person. If a person is resting at a certain location, the risk of being exposed to a pollutant is lower than a situation in which the person is running at the same location. If the person is running, they are inhaling more air, thus more pollution, than when they are resting. In this thesis we propose a methodology to personalize the risk of being exposed to a certain pollutant. In Chapter 2 we talk about a possible way to estimate the PM2.5 concentration at any location in Chicago. In Chapter 3 we discuss a methodology to estimate the heart rate of the user, while Chapter 4 contains methods to compute, estimate and personalize the minute ventilation. Chapter 5 illustrates how the values estimated in the previous chapters are used to show to the user the personalized risk. In Chapter 5 we also talk about how we built the MY-AIR app and the data that we have used.

History

Advisor

Wolfson, Ouri

Chair

Wolfson, Ouri

Department

Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Masters

Degree name

MS, Master of Science

Committee Member

Lin, Jane Sistla, Aravinda Patti, Edoardo

Submitted date

August 2022

Thesis type

application/pdf

Language

  • en

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