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An innovative way to reduce COVID spread and also aid contact tracing

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Kamal Saini
Kamal S. has been Journalist and Writer for Business, Hardware and Gadgets at Revyuh.com since 2018. He deals with B2b, Funding, Blockchain, Law, IT security, privacy, surveillance, digital self-defense and network policy. As part of his studies of political science, sociology and law, he researched the impact of technology on human coexistence. Email: kamal (at) revyuh (dot) com

During the COVID-19 pandemic, many healthcare and security employees risked their health to serve the public. This new work enables them to serve their communities safely and efficiently.

A mobile robot recognizes people in gatherings who are not following social-distancing guidelines, navigates to them, and encourages them to move apart as part of a novel method to combat COVID-19 spread.

On December 1, 2021, Adarsh Jagan Sathyamoorthy of the University of Maryland, College Park, and colleagues published their findings in the open-access journal PLOS ONE.

COVID-19 can be reduced by staying at least two meters apart from people, according to previous research. Technology-based solutions, such as WiFi and Bluetooth strategies, have the potential to detect and prevent social distance lapses.

However, since most of these systems rely on people engagement or existing infrastructure, robots have emerged as a viable solution for tackling social distancing in crowds.

Now, Sathyamoorthy and colleagues have devised a revolutionary method for accomplishing this goal by employing an autonomous mobile robot. Using its own RGB-D camera and 2-D LiDAR (Light Detection and Ranging) sensor, the robot can identify breaches and travel to them, as well as tap into an existing CCTV system if one is available. When the robot approaches the breach, it uses letters on a mounted display to encourage people to move away.

The robot employs a revolutionary algorithm to categorize persons who have broken social distance rules into several groups, prioritize them based on whether they are static or moving, and then navigate to them. This system uses Deep Reinforcement Learning, a machine-learning technology, and Frozone, an algorithm originally created by several of the same researchers to assist robots in navigating crowds.

The researchers validated their strategy by having participants act out scenarios involving social distancing breaches while standing motionless, walking, or moving in an irregular manner. Their robot was able to detect and address the majority of breaches, and CCTV greatly improved its performance.

Additionally, the robot incorporates a thermal camera that may detect people who may be suffering from fevers, assisting in contact tracing operations while also ensuring privacy protection and de-identification.

Additional research is required to validate and develop this strategy, for example, by examining how the presence of robots affects crowd behavior.

According to the authors: “A lot of healthcare workers and security personnel had to put their health at risk to serve the public during the COVID-19 pandemic. Our work’s core objective is to provide them with tools to safely and efficiently serve their communities.”

Source: 10.1371/journal.pone.0259713

Image Credit: Getty

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