Login | August 13, 2020

OSU engineer says smartphones would help in COVID tracking

KEITH ARNOLD
Special to the Legal News

Published: July 13, 2020

The typical smartphone in the pocket or bag of any central Ohioan could prove instrumental in the tracking of the coronavirus as the Buckeye State experiences an increase in the number of reported cases in recent weeks.
According to Ohio State University computer science and engineering professor Dong Xuan, the signals sent to and received by cellphone microphones and speakers could help warn people when they have been near someone who has contracted COVID-19.
Xuan and a group of researchers described a system that would generate random, anonymous IDs for each phone, automatically send ultrasonic signals between microphones and speakers of phones within a certain radius, and use the information exchanged through this acoustic channel for contact tracing.
The research was posted - without peer review - on the arXiv pre-print server, a press release noted.
Once a person tests positive for COVID-19, the individual would update the anonymous IDs and the timestamp when the IDs were generated in the past two weeks to a central database managed by a trusted health-care authority, the research detailed.
Each individual in the system would pull the positive patient's IDs and compare locally to check whether any contact with the patient has occurred.
"We want to generate some kind of sound that cannot be heard by humans, but that phones can hear," Xuan said. "The phone will periodically generate some kind of sound token and send that token to nearby phones - and the key advantage over other technologies is that the ultrasound could have limited range and will not penetrate obstacles such as walls."
Tech companies already have proposed using a phone's Bluetooth capability to build such a network, according to published accounts Bluetooth, however, could lead to a high number of false-positive close contacts, said the paper's co-author Zhiqiang Lin, an assistant professor of computer science and engineering.
"Bluetooth has a problem of traveling too far," Lin said. "Bluetooth signals can travel through walls and reach further than we would want. And with COVID, we want to find just those people with whom you have been in direct close contact - within that 6-foot radius, for example."
Identifying people who might have been exposed to a person who has tested positive for the virus, or contact tracing, has been a key part of the public health strategy to stop the spread of illnesses for decades.
The practice in the current pandemic has been difficult, however. People might remember who they met for dinner before symptoms appeared, but they likely would not know how to contact the strangers who were near them in the grocery store, researchers said.
"It's hard for people to remember who they had contact with, and augmenting manual contact tracing with automated techniques could greatly increase its reliability," said Ohio Eminent Scholar in networking and communications Ness Shroff.
Shroff is leading the university's efforts to develop an automated system for contact tracing and symptom reporting for the return to full operations.
Cellphone tracing could solve that problem, he said - as long as it is accurate and provides a satisfactory level of privacy, and as long as people use it.
Researchers cited a failed attempt in Singapore in which too few people downloaded and used the app. Additionally, the app picked up contact between people who were in the same vicinity, but separated by walls or windows.
Shroff said the acoustic sensors might offer more control in the effort to trace the virus.
"In addition to exploring Bluetooth, we want to leverage other sensors in the phone to do contact tracing," Xuan said. "The key advantage of this work is that it lets a phone search for complementary sensors and uses the sensors to detect proximity. That is something that can complement the Bluetooth technology."
Other Ohio State researchers on this work include Adam Champion, Yuxiang Luo, Cheng Zhang, Yunqi Zhang, and Chaoshun Zuo.
The work is supported in part by an NSF RAPID grant for defending against COVID-19.
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