FEATURES Pneumonia Identier
research, and I pivoted so many times during
this period to keep the dream alive of quickly
diagnosing fungal pneumonia — and possibly
other respiratory diseases. Nothing would get in
my way. By accident, I found an article in Make:
Volume 77, by a gentleman named Benjamin
Cabé. Benjamin had created an E-Nose to detect
sourdough starter, whiskey(s), and coffee. The
E-Nose information in this article was priceless
because it gave me the opportunity to use
Benjamin’s framework to build my own E-Nose.
I cross-referenced several studies, including
Benjamin’s original prototype, and determined
that the ideal sensor array for my project would
include Seeed Studio’s multichannel gas sensor,
with nitrogen dioxide (NO
2
), carbon monoxide
(CO), ethyl alcohol (C
2
H
5
OH), and volatile organic
compounds sensors, and potentially a more
extensive sensory array including Seeed’s MQ9
(carbon monoxide, flammable gases), MQ4
(methane), MQ135 (ammonia, benzene, NOx),
MQ8 (hydrogen), TGS2620 (alcohols, organic
solvents), MQ136 (sulfur dioxide), TGS813
(hydrocarbons), TGS822 (organic solvents), and
MQ3 (alcohols).
The E-Nose build would utilize the Seeed Wio
Terminal (2.4" LCD screen, ATSAMD51 core,
and Realtek RTL8720DN radio module with
BLE 5.0 and Wi-Fi 2.4GHz/5GHz), utilizing its I
2
C
interface, MOSFET fan control, Seeed-based
sensor arrays, and Seeed expansion battery
pack to enable wireless connectivity.
MY PROCEDURE
I felt it was important to keep this as close to a
human patient study as possible. I constructed a
cleanroom out of a storage container, complete
with gloves and a mechanical artificial lung that
would breathe in the essential oil sample then
breathe it out into another case that contained
the E-Nose for sampling.
Next, and most importantly, cloning the
framework of Benjamin’s project I interfaced
my E-Nose with Edge Impulse. Edge Impulse
is a platform that takes the data you collect
through supported microcontrollers and builds
an artificial intelligence (AI) that can be deployed
back into your microcontroller. Effectively Edge
Impulse allowed the AI to “smell” through
the connected sensors by identifying the four
distinct monoterpenes/terpenes of fungal
pneumonia, separately and mixed. Though each
sample took only minutes, the combination of
samples added up to hours of sample taking
for each chemical compound, to aid the AI in
differentiating between each chemical. Once
sampling was complete my E-Nose had a
96.5% accuracy before deployment and 87.7%
optimized deployment.
One of the most important components of this
experiment was to make this E-Nose accessible
online in real-time. Benjamin’s project also
included the ability to do this. Updating the
necessary firmware to the Seeed Wio Terminal,
I created an account on Microsoft’s Azure IoT
platform. Then I had a Zoom call with Benjamin,
who is based in France.
Benjamin downloaded my E-Nose framework
ONCE SAMPLING WAS COMPLETE
MY E-NOSE HAD A 96.5% ACCURACY
BEFORE DEPLOYMENT AND 87.7%
OPTIMIZED DEPLOYMENT.
18
makezine.com
Heather Kodama (aka Mom)
My E-Nose prototype and various sensors.
My DIY cleanroom with mechanical artificial lung.
M81_016-19_PneumoniaNose_F1.indd 18M81_016-19_PneumoniaNose_F1.indd 18 4/12/22 1:34 PM4/12/22 1:34 PM
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