Medical Design Briefs - December 2022 - 27

In-Home Wireless Device Tracks Disease Progression in
Parkinson's Patients
By continuously monitoring
a patient's gait speed, the
system can assess the
condition's severity
between visits to the
doctor's office.
MIT
Cambridge, MA
Parkinson's disease, the world's fastestgrowing
neurological disease, now affects
more than 10 million people worldwide,
yet clinicians still face huge challenges in
tracking its severity and progression.
Clinicians typically evaluate patients by
testing their motor skills and cognitive
functions during clinic visits. These
semisubjective measurements are often
skewed by outside factors - perhaps a patient
is tired after a long drive to the hospital.
More than 40 percent of individuals
with Parkinson's are never treated by a
neurologist or Parkinson's specialist, often
because they live too far from an urban
center or have difficulty traveling.
In an effort to address these problems,
researchers from MIT and elsewhere
demonstrated an in-home device
that can monitor a patient's movement
and gait speed, which can be used to
evaluate Parkinson's severity, the progression
of the disease, and the patient's
response to medication.
The device, which is about the size of a
Wi-Fi router, gathers data passively using
radio signals that reflect off the patient's
body as they move around their home.
The patient does not need to wear a gadget
or change their behavior. (A recent
study, for example, showed that this type
of device could be used to detect Parkinson's
from a person's breathing patterns
while sleeping.)
The researchers used these devices to
conduct a one-year at-home study with 50
participants. They showed that, by using
machine-learning algorithms to analyze
the troves of data they passively gathered
(more than 200,000 gait speed measurements),
clinicians could track Parkinson's
progression and medication response
more effectively than they would with periodic,
in-clinic evaluations.
Medical Design Briefs, December 2022
By continuously monitoring a patient's gait speed, an in-home wireless system can assess the condition's
severity between visits to the doctor's office. (Credit: N.Fuller, SayoStudio)
" By being able to have a device in the
home that can monitor a patient and
tell the doctor remotely about the progression
of the disease, and the patient's
medication response so they can attend
to the patient even if the patient can't
come to the clinic - now they have real,
reliable information - that actually
goes a long way toward improving equity
and access, " says senior author Dina Katabi,
the Thuan and Nicole Pham Professor
in the department of electrical engineering
and computer science (EECS),
and a principle investigator in the Computer
Science and Artificial Intelligence
Laboratory (CSAIL) and the MIT Jameel
Clinic.
n A Human Radar
This work utilizes a wireless device previously
developed in the Katabi lab that
analyzes radio signals that bounce off
people's bodies. It transmits signals that
use a tiny fraction of the power of a Wi-Fi
router - these super-low-power signals
don't interfere with other wireless devices
in the home. While radio signals pass
through walls and other solid objects,
they are reflected off humans due to the
water in our bodies.
This creates a human radar that can
track the movement of a person in a room.
Radio waves always travel at the same
speed, so the length of time it takes the signals
to reflect back to the device indicates
how the person is moving.
www.medicaldesignbriefs.com
The device incorporates a machinelearning
classifier that can pick out the
precise radio signals reflected off the patient
even when there are other people
moving around the room. Advanced algorithms
use these movement data to
compute gait speed - how fast the person
is walking.
Because the device operates in the
background and runs all day, every day,
it can collect a massive amount of data.
The researchers wanted to see whether
they could apply machine learning to
these datasets to gain insights about the
disease over time.
They gathered 50 participants, 34 of
whom had Parkinson's, and conducted a
one-year study of in-home gait measurements.
Through the study, the researchers
collected more than 200,000 individual
measurements that they averaged to
smooth out variability due to the conditions
irrelevant to the disease. (For example,
a patient may hurry up to answer
an alarm or walk slower when talking on
the phone.)
They used statistical methods to analyze
the data and found that in-home
gait speed can be used to effectively
track Parkinson's progression and severity.
For instance, they showed that gait
speed declined almost twice as fast for
individuals with Parkinson's, compared
to those without.
" Monitoring the patient continuously
as they move around the room enabled
27
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Medical Design Briefs - December 2022

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Medical Design Briefs - December 2022 - COV1A
Medical Design Briefs - December 2022 - COV1B
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