Health Modeling Using Smart Device Data; Physiological Data Privacy in the Digital Age

Johan, Aldrick, School of Engineering and Applied Science, University of Virginia
Ku, Tsai-Hsuan, EN-Engineering and Society, University of Virginia

Due to the severity of the COVID-19 crisis, governments have been scrambling to find
any means to slow down or prevent any spreading of the disease. They have used various
methods to hold back the disease such as quarantining, limiting travel, and even using apps for
contact tracing and detecting the illness. This contact tracing is accomplished by collecting
physiological data from mobile phones and wearable devices. Anybody who uses their
smartphone or wears a “smart” device is subject to this collection of data. This data is collected
by large companies employed by governments to supposedly detect signs of the illness.
However, the collection of this data can violate a person’s privacy or be used malevolently. The
use of smart devices to monitor the spread of COVID-19 is a pertinent example that correlates
the topic of physiological data privacy with the use of smart device data for health modeling.
The STS study explored the concept of privacy in relation to physiological data
collection. As wearable devices become more common, the topic of physiological data collection
will become more crucial. The topic was explored through the use of surveys, interviews, and
document review. The technical study connects to the STS topic by exploring how personal
health data collected from smart devices can be used. One of the supposed benefits of collecting
personal health data is that it can be utilized to detect mental and physical illnesses in users. The
study explored this idea and utilized machine learning to glean useful information from a user’s
personal health data. The study also explored what data was available from the smart devices and
the amount of personal data that was present in the data.

BS (Bachelor of Science)
Data privacy, Smart devices, Modeling

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Afsaneh Doryab
STS Advisor: Tsai-Hsuan Ku
Technical Team Members: Keshav Ailaney, Wei Wang, Johan Ketkar

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