Tech Briefs Magazine - March 2022 - 50

Software
Computer Vision App for Monitoring Diabetes
The mobile phone app enables regular monitoring of glucose levels in people with diabetes.
University of Cambridge, United Kingdom
R
esearchers have developed computer
vision techniques to read and record
glucose levels, time, and date displayed
on a typical glucose test via the camera
on a mobile phone. The technology,
which doesn't require an inter net or
Bluetooth connection, works for any
type of glucose meter, in any orientation,
and in a variety of light levels. It also reduces
waste by eliminating the need to
replace high-quality non-Blue tooth meters,
making it a cost-effective solution.
The team created a free mobile phone
app, called GlucoRx Vision. Users simply
take a picture of their glucose meter and
the results are automatically read and recorded,
allowing much easier monitoring
of blood glucose levels.
In addition to the glucose meters that
people with diabetes use on a daily basis,
many other types of digital meters are used
in the medical and industrial sectors; however,
many of these meters still do not have
wireless connectivity, so connecting them
to phone tracking apps often requires
manual input.
The computer vision technology be hind
the GlucoRx app is made up of two steps.
First, the screen of the glucose meter is detected.
The researchers used a single training
image and augmented it with random
Reading the diabetes monitor. (Photo: James Charles)
backgrounds,
particularly backgrounds
with people. This helps ensure the system
is robust when the user's face is reflected
in the phone's screen.
Second, a neural network called LeDigit
detects each digit on the screen and
reads it. The network is trained with computer-generated
synthetic data, avoiding
the need for labor-intensive labeling of
data, which is commonly needed to train
a neural network. Tests were conducted in
various types of orientations, viewpoints,
and light levels. The app will vibrate when
AI Technology Foils Cyberattacks
The technology is aimed at protecting physical targets, such as utilities and infrastructure.
Pacific Northwest National Laboratory, Richland, WA
S
cientists have created a cybersecurity
technology called Shadow Figment
that is designed to lure hackers
into an artificial world, then stop them
from doing damage by feeding them
illusory tidbits of success. The aim is
to sequester bad actors by captivating
them with an attractive, but imaginary
world. The technology is aimed
at protecting physical targets - infrastructure
such as buildings, the electric
grid, water and sewage systems, and
pipelines.
50
The starting point for Shadow Figment
is an oft-deployed technology
called a honeypot, which is something
attractive to lure an attacker - perhaps
a desirable target with the appearance
of easy access. While most honeypots
are used to lure attackers and study
their methods, Shadow Figment goes
much further. The technology uses artificial
intelligence to deploy elaborate
deception to keep attackers engaged in
a pretend world (the figment) that mirrors
the real world. The decoy interacts
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with users in real time, responding in
realistic ways to commands.
The system rewards hackers with false
signals of success, keeping them occupied
while defenders learn about the
attackers' methods and take actions to
protect the real system. The credibility
of the deception relies on a machine
learning program that learns from observing
the real-world system where it is
installed. The program responds to an
attack by sending signals that illustrate
that the system under attack is respondTech
Briefs, March 2022
it has read the information, so the user
gets a clear signal when they have done
it correctly. The system is accurate across
a range of different types of meters, with
read accuracies close to 100 percent.
In addition to blood glucose monitoring,
the researchers also tested the system
on different types of digital meters, such as
blood pressure monitors and kitchen and
bathroom scales.
For more information, contact Sarah Collins
at sarah.collins@admin.cam.ac.uk; +44
(0)1223 765542.
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Tech Briefs Magazine - March 2022

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